{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:33:37Z","timestamp":1792100017747,"version":"4.4.0"},"reference-count":271,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001782","name":"University of Melbourne","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001782","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.<\/jats:p>","DOI":"10.1007\/s10462-024-10934-9","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T02:02:15Z","timestamp":1726452135000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["A survey of video-based human action recognition in team sports"],"prefix":"10.1007","volume":"57","author":[{"given":"Hongwei","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard O.","family":"Sinnott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glenn T.","family":"Jayaputera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"issue":"1","key":"10934_CR1","doi-asserted-by":"publisher","first-page":"72","DOI":"10.18178\/joig.11.1.72-81","volume":"11","author":"MA Abdelrazik","year":"2023","unstructured":"Abdelrazik MA, Zekry A, Mohamed WA (2023) Efficient hybrid algorithm for human action recognition. J Image Graphics 11(1):72\u201381","journal-title":"J Image Graphics"},{"key":"10934_CR2","unstructured":"Abu-El-Haija S, Kothari N, Lee J, Natsev P, Toderici G, Varadarajan B, Vijayanarasimhan S (2016) YouTube-8M: a large-scale video classification benchmark. arXiv arXiv: 1609.08675"},{"key":"10934_CR3","unstructured":"Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, Almeida D, Altenschmidt J, Altman S, Anadkat S et\u00a0al (2023) Gpt-4 technical report. arXiv preprint arXiv:2303.08774"},{"issue":"11","key":"10934_CR4","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311\u20134322. https:\/\/doi.org\/10.1109\/TSP.2006.881199","journal-title":"IEEE Trans Signal Process"},{"issue":"3","key":"10934_CR5","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s00530-022-01027-0","volume":"29","author":"S Akan","year":"2023","unstructured":"Akan S, Varli S (2023) Use of deep learning in soccer videos analysis: survey. Multimed Syst 29(3):897\u2013915. https:\/\/doi.org\/10.1007\/s00530-022-01027-0","journal-title":"Multimed Syst"},{"issue":"16","key":"10934_CR6","doi-asserted-by":"publisher","first-page":"6080","DOI":"10.3390\/s22166080","volume":"22","author":"A Aldayri","year":"2022","unstructured":"Aldayri A, Albattah W (2022) Taxonomy of anomaly detection techniques in crowd scenes. Sensors 22(16):6080. https:\/\/doi.org\/10.3390\/s22166080","journal-title":"Sensors"},{"issue":"5","key":"10934_CR7","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/s42979-020-00293-x","volume":"1","author":"R Alfaifi","year":"2020","unstructured":"Alfaifi R, Artoli AM (2020) Human action prediction with 3D-CNN. SN Comput Sci 1(5):286. https:\/\/doi.org\/10.1007\/s42979-020-00293-x","journal-title":"SN Comput Sci"},{"key":"10934_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108243","volume":"133","author":"Y An","year":"2024","unstructured":"An Y, Yi Y, Han X, Wu L, Su C, Liu B, Xue X, Li Y (2024) A hybrid attention-guided convnext-gru network for action recognition. Eng Appl Artif Intell 133:108243","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"10934_CR9","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1136\/bjsm.2003.007955","volume":"38","author":"TE Andersen","year":"2004","unstructured":"Andersen TE, Tenga A, Engebretsen L, Bahr R (2004) Video analysis of injuries and incidents in Norwegian professional football. Br J Sports Med 38(5):626\u2013631. https:\/\/doi.org\/10.1136\/bjsm.2003.007955","journal-title":"Br J Sports Med"},{"key":"10934_CR10","doi-asserted-by":"publisher","unstructured":"Arandjelovic R, Zisserman A (2012) Three things everyone should know to improve object retrieval. In: 2012 IEEE conference on computer vision and pattern recognition, pp 2911\u20132918. https:\/\/doi.org\/10.1109\/CVPR.2012.6248018","DOI":"10.1109\/CVPR.2012.6248018"},{"issue":"6","key":"10934_CR11","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1177\/0363546504262973","volume":"32","author":"A Arnason","year":"2004","unstructured":"Arnason A, Tenga A, Engebretsen L, Bahr R (2004) A prospective video-based analysis of injury situations in elite male football: football incident analysis. Am J Sports Med 32(6):1459\u20131465. https:\/\/doi.org\/10.1177\/0363546504262973","journal-title":"Am J Sports Med"},{"key":"10934_CR12","doi-asserted-by":"crossref","unstructured":"Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2010) Action classification in soccer videos with long short-term memory recurrent neural networks. In: International conference on artificial neural networks, vol 6353. Springer, New York, pp 154\u2013159","DOI":"10.1007\/978-3-642-15822-3_20"},{"key":"10934_CR13","doi-asserted-by":"publisher","unstructured":"Ballan L, Bertini M, Bimbo AD, Serra G (2009) Action categorization in soccer videos using string kernels. In: 2009 seventh international workshop on content-based multimedia indexing, pp 13\u201318. https:\/\/doi.org\/10.1109\/CBMI.2009.10","DOI":"10.1109\/CBMI.2009.10"},{"issue":"3","key":"10934_CR14","doi-asserted-by":"publisher","first-page":"4341","DOI":"10.1007\/s11042-021-11784-1","volume":"81","author":"S Barbon Junior","year":"2022","unstructured":"Barbon Junior S, Pinto A, Barroso JV, Caetano FG, Moura FA, Cunha SA, Torres RDS (2022) Sport action mining: dribbling recognition in soccer. Multimed Tools Appl 81(3):4341\u20134364. https:\/\/doi.org\/10.1007\/s11042-021-11784-1","journal-title":"Multimed Tools Appl"},{"key":"10934_CR15","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","volume-title":"Computer vision\u2014ECCV 2006","author":"H Bay","year":"2006","unstructured":"Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision\u2014ECCV 2006. Springer, Berlin, pp 404\u2013417"},{"key":"10934_CR16","doi-asserted-by":"publisher","unstructured":"Bayoudh K, Hamdaoui F, Mtibaa A (2022) An attention-based hybrid 2D\/3D CNN-LSTM for human action recognition. In: 2022 2nd international conference on computing and information technology (ICCIT), pp 97\u2013103. https:\/\/doi.org\/10.1109\/ICCIT52419.2022.9711631","DOI":"10.1109\/ICCIT52419.2022.9711631"},{"key":"10934_CR17","unstructured":"Bertasius G, Wang H, Torresani L (2021) Is space-time attention all you need for video understanding? CoRR 2, 4. arXiv: 2102.05095"},{"key":"10934_CR18","doi-asserted-by":"publisher","unstructured":"Biermann H, Theiner J, Bassek M, Raabe D, Memmert D, Ewerth R (2021) A unified taxonomy and multimodal dataset for events in invasion games. In: Proceedings of the 4th international workshop on multimedia content analysis in sports. MMSports\u201921. Association for Computing Machinery, New York, NY, USA. event-place: Virtual Event, China, pp 1\u201310. https:\/\/doi.org\/10.1145\/3475722.3482792","DOI":"10.1145\/3475722.3482792"},{"key":"10934_CR19","doi-asserted-by":"publisher","unstructured":"Blank M, Gorelick L, Shechtman E, Irani M, Basri R (2005) Actions as space-time shapes. In: Tenth IEEE international conference on computer vision (ICCV\u201905), vol 1\u20132, pp 1395\u201314022. https:\/\/doi.org\/10.1109\/ICCV.2005.28","DOI":"10.1109\/ICCV.2005.28"},{"key":"10934_CR20","doi-asserted-by":"crossref","unstructured":"Bo,Y, Lu Y, He W (2020) Few-shot learning of video action recognition only based on video contents. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV), pp 595\u2013604","DOI":"10.1109\/WACV45572.2020.9093481"},{"key":"10934_CR21","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-031-45170-6_47","volume-title":"Pattern recognition and machine intelligence","author":"S Bose","year":"2023","unstructured":"Bose S, Sarkar S, Chakrabarti A (2023) SoccerKDNet: a knowledge distillation framework for action recognition in soccer videos. In: Maji P, Huang T, Pal NR, Chaudhury S, De RK (eds) Pattern recognition and machine intelligence, vol 14301. Springer, Cham, pp 457\u2013464"},{"issue":"6","key":"10934_CR22","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s00138-021-01249-8","volume":"32","author":"SY Boulahia","year":"2021","unstructured":"Boulahia SY, Amamra A, Madi MR, Daikh S (2021) Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition. Mach Vis Appl 32(6):121. https:\/\/doi.org\/10.1007\/s00138-021-01249-8","journal-title":"Mach Vis Appl"},{"issue":"2","key":"10934_CR23","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3390\/mca28020061","volume":"28","author":"F Camarena","year":"2023","unstructured":"Camarena F, Gonzalez-Mendoza M, Chang L, Cuevas-Ascencio R (2023) An overview of the vision-based human action recognition field. Math Comput Appl 28(2):61. https:\/\/doi.org\/10.3390\/mca28020061","journal-title":"Math Comput Appl"},{"key":"10934_CR24","doi-asserted-by":"crossref","unstructured":"Cao Z, Hidalgo G, Simon T, Wei S-E, Sheikh Y (2018) OpenPose: realtime multi-person 2D pose estimation using part affinity fields. CoRR abs\/1812.08008, 7291\u20137299. arXiv:1812.08008","DOI":"10.1109\/CVPR.2017.143"},{"key":"10934_CR25","unstructured":"Careaga C, Hutchinson B, Hodas NO, Phillips L (2019) Metric-based few-shot learning for video action recognition. CoRR abs\/1909.09602. arXiv:1909.09602"},{"key":"10934_CR26","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. CoRR 12346, pp 213\u2013229. arXiv:2005.12872","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"10934_CR29","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. CoRR abs\/1705.07750, 6299\u20136308. arXiv:1705.07750","DOI":"10.1109\/CVPR.2017.502"},{"key":"10934_CR27","unstructured":"Carreira J, Noland E, Banki-Horvath A, Hillier C, Zisserman A (2018) A Short Note about Kinetics-600. CoRR abs\/1808.01340. arXiv:1808.01340"},{"key":"10934_CR28","unstructured":"Carreira J, Noland E, Hillier C, Zisserman A (2019) A Short Note on the Kinetics-700 human action dataset. CoRR abs\/1907.06987. arXiv:1907.06987"},{"issue":"7","key":"10934_CR30","doi-asserted-by":"publisher","first-page":"7034","DOI":"10.1109\/JSEN.2022.3150225","volume":"22","author":"X Chao","year":"2022","unstructured":"Chao X, Hou Z, Mo Y (2022) CZU-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors. IEEE Sens J 22(7):7034\u20137042","journal-title":"IEEE Sens J"},{"key":"10934_CR31","doi-asserted-by":"publisher","unstructured":"Chebli K, Khalifa AB (2018) Pedestrian detection based on background compensation with block-matching algorithm. In: 2018 15th international multi-conference on systems, signals & devices (SSD), pp 497\u2013501. https:\/\/doi.org\/10.1109\/SSD.2018.8570499","DOI":"10.1109\/SSD.2018.8570499"},{"key":"10934_CR32","doi-asserted-by":"publisher","first-page":"9993677","DOI":"10.1155\/2021\/9993677","volume":"2021","author":"X Chen","year":"2021","unstructured":"Chen X, Yuan G (2021) Sports injury rehabilitation intervention algorithm based on visual analysis technology. Mob Inf Syst 2021:9993677. https:\/\/doi.org\/10.1155\/2021\/9993677. (Publisher: Hindawi)","journal-title":"Mob Inf Syst"},{"key":"10934_CR35","doi-asserted-by":"publisher","unstructured":"Chen C, Jafari R, Kehtarnavaz N (2015) UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: 2015 IEEE international conference on image processing (ICIP), pp 168\u2013172. https:\/\/doi.org\/10.1109\/ICIP.2015.7350781","DOI":"10.1109\/ICIP.2015.7350781"},{"issue":"1","key":"10934_CR33","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11554-013-0370-1","volume":"12","author":"C Chen","year":"2016","unstructured":"Chen C, Liu K, Kehtarnavaz N (2016) Real-time human action recognition based on depth motion maps. J Real-Time Image Proc 12(1):155\u2013163. https:\/\/doi.org\/10.1007\/s11554-013-0370-1","journal-title":"J Real-Time Image Proc"},{"issue":"18","key":"10934_CR34","doi-asserted-by":"publisher","first-page":"27551","DOI":"10.1007\/s11042-023-14490-2","volume":"82","author":"C-C Chen","year":"2023","unstructured":"Chen C-C, Chang C, Lin C-S, Chen C-H, Chen IC (2023) Video based basketball shooting prediction and pose suggestion system. Multimed Tools Appl 82(18):27551\u201327570. https:\/\/doi.org\/10.1007\/s11042-023-14490-2","journal-title":"Multimed Tools Appl"},{"key":"10934_CR36","doi-asserted-by":"crossref","unstructured":"Chen B, Shvetsova N, Rouditchenko A, Kondermann D, Thomas S, Chang S-F, Feris R, Glass J, Kuehne H (2024) What when and where? Self-supervised spatio-temporal grounding in untrimmed multi-action videos from narrated instructions. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 18419\u201318429","DOI":"10.1109\/CVPR52733.2024.01743"},{"key":"10934_CR38","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"10934_CR37","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-540-78566-8_10","volume-title":"Virtual systems and multimedia","author":"J Choi","year":"2008","unstructured":"Choi J, Cho Y-I, Han T, Yang HS (2008) A view-based real-time human action recognition system as an interface for human computer interaction. In: Wyeld TG, Kenderdine S, Docherty M (eds) Virtual systems and multimedia, vol 4820. Springer, Berlin, pp 112\u2013120"},{"issue":"1","key":"10934_CR39","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1038\/s41597-022-01469-1","volume":"9","author":"A Cioppa","year":"2022","unstructured":"Cioppa A, Deli\u00e8ge A, Giancola S, Ghanem B, Van Droogenbroeck M (2022a) Scaling up SoccerNet with multi-view spatial localization and re-identification. Sci Data 9(1):355. https:\/\/doi.org\/10.1038\/s41597-022-01469-1","journal-title":"Sci Data"},{"key":"10934_CR41","doi-asserted-by":"crossref","unstructured":"Cioppa A, Giancola S, Deli\u00e8ge A, Kang L, Zhou X, Cheng Z, Ghanem B, Van\u00a0Droogenbroeck M (2022b) SoccerNet-tracking: multiple object tracking dataset and benchmark in soccer videos","DOI":"10.1109\/CVPRW56347.2022.00393"},{"key":"10934_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/s12283-024-00466-4","volume-title":"SoccerNet 2023 challenges results","author":"A Cioppa","year":"2024","unstructured":"Cioppa A, Giancola S, Somers V, Magera F, Zhou X, Mkhallati H, Deli\u00e8ge A, Held J, Hinojosa C, Mansourian AM et al (2024) SoccerNet 2023 challenges results. Springer, New York"},{"key":"10934_CR42","doi-asserted-by":"publisher","first-page":"4351435","DOI":"10.1155\/2016\/4351435","volume":"2016","author":"E Cippitelli","year":"2016","unstructured":"Cippitelli E, Gasparrini S, Gambi E, Spinsante S (2016) A human activity recognition system using skeleton data from RGBD sensors. Comput Intell Neurosci 2016:4351435. https:\/\/doi.org\/10.1155\/2016\/4351435","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"10934_CR43","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40798-019-0202-3","volume":"5","author":"JG Claudino","year":"2019","unstructured":"Claudino JG, Capanema DDO, De Souza TV, Serr\u00e3o JC, Machado Pereira AC, Nassis GP (2019) Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports Med-Open 5(1):28. https:\/\/doi.org\/10.1186\/s40798-019-0202-3","journal-title":"Sports Med-Open"},{"key":"10934_CR44","unstructured":"Dai R, Das S, Kahatapitiya K, Ryoo MS, Br\u00e9mond F (2021) MS-TCT: multi-scale temporal ConvTransformer for action detection. CoRR abs\/2112.03902, 20041\u201320051. arXiv: 2112.03902"},{"key":"10934_CR46","doi-asserted-by":"publisher","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol 1, pp 886\u20138931. https:\/\/doi.org\/10.1109\/CVPR.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"key":"10934_CR45","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/11744047_33","volume-title":"Computer vision\u2014ECCV 2006","author":"N Dalal","year":"2006","unstructured":"Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision\u2014ECCV 2006, vol 3952. Springer, Berlin, pp 428\u2013441"},{"issue":"1","key":"10934_CR47","doi-asserted-by":"publisher","first-page":"51","DOI":"10.18280\/ijsse.130106","volume":"13","author":"VK Damera","year":"2023","unstructured":"Damera VK, Vatambeti R, Mekala MS, Pani AK, Manjunath C (2023) Normalized attention neural network with adaptive feature recalibration for detecting the unusual activities using video surveillance camera. Int J Saf Secur Eng 13(1):51\u201358. https:\/\/doi.org\/10.18280\/ijsse.130106","journal-title":"Int J Saf Secur Eng"},{"key":"10934_CR48","unstructured":"De\u00a0Vleeschouwer C, Chen F, Delannay D, Parisot C, Chaudy C, Martrou E, Cavallaro A et\u00a0al (2008) Distributed video acquisition and annotation for sport-event summarization. NEM summit"},{"key":"10934_CR49","doi-asserted-by":"crossref","unstructured":"Deli\u00e8ge A, Cioppa A, Giancola S, Seikavandi MJ, Dueholm JV, Nasrollahi K, Ghanem B, Moeslund TB, Droogenbroeck MV (2020) SoccerNet-v2 : a dataset and benchmarks for holistic understanding of broadcast soccer videos. CoRR abs\/2011.13367, 4508\u20134519. arXiv: 2011.13367","DOI":"10.1109\/CVPRW53098.2021.00508"},{"key":"10934_CR50","doi-asserted-by":"crossref","unstructured":"Denize J, Liashuha M, Rabarisoa J, Orcesi A, H\u00e9rault R (2024) Comedian: self-supervised learning and knowledge distillation for action spotting using transformers","DOI":"10.1109\/WACVW60836.2024.00060"},{"key":"10934_CR51","doi-asserted-by":"publisher","first-page":"3835","DOI":"10.1109\/TIP.2020.2965299","volume":"29","author":"C Dhiman","year":"2020","unstructured":"Dhiman C, Vishwakarma DK (2020) View-invariant deep architecture for human action recognition using two-stream motion and shape temporal dynamics. IEEE Trans Image Process 29:3835\u20133844. https:\/\/doi.org\/10.1109\/TIP.2020.2965299","journal-title":"IEEE Trans Image Process"},{"key":"10934_CR52","doi-asserted-by":"crossref","unstructured":"Dhulekar P, Gandhe ST, Chitte H, Pardeshi K (2017) Human action recognition: an overview. In: Satapathy SC, Bhateja V, Joshi A (eds) Proceedings of the international conference on data engineering and communication technology. Springer, Singapore, pp 481\u2013488","DOI":"10.1007\/978-981-10-1675-2_48"},{"key":"10934_CR53","unstructured":"Diba A, Fayyaz M, Sharma V, Paluri M, Gall J, Stiefelhagen R, Gool LV (2019) Holistic large scale video understanding. CoRR 38:9 arXiv: 1904.11451"},{"key":"10934_CR54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3327284","author":"G Ding","year":"2024","unstructured":"Ding G, Sener F, Yao A (2024) Temporal action segmentation: an analysis of modern. Techniques. https:\/\/doi.org\/10.1109\/TPAMI.2023.3327284","journal-title":"Techniques"},{"key":"10934_CR55","doi-asserted-by":"publisher","unstructured":"Dollar P, Rabaud V, Cottrell G, Belongie S (2005) Behavior recognition via sparse spatio-temporal features. In: 2005 IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, pp 65\u201372. https:\/\/doi.org\/10.1109\/VSPETS.2005.1570899","DOI":"10.1109\/VSPETS.2005.1570899"},{"key":"10934_CR56","doi-asserted-by":"crossref","unstructured":"Donahue J, Hendricks LA, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2014) Long-term recurrent convolutional networks for visual recognition and description. CoRR abs\/1411.4389, 2625\u20132634. arXiv: 1411.4389","DOI":"10.1109\/CVPR.2015.7298878"},{"issue":"8","key":"10934_CR57","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1016\/j.patcog.2010.03.009","volume":"43","author":"T D\u2019Orazio","year":"2010","unstructured":"D\u2019Orazio T, Leo M (2010) A review of vision-based systems for soccer video analysis. Pattern Recogn 43(8):2911\u20132926. https:\/\/doi.org\/10.1016\/j.patcog.2010.03.009","journal-title":"Pattern Recogn"},{"key":"10934_CR58","doi-asserted-by":"publisher","unstructured":"D\u2019Orazio T, Leo M, Mosca N, Spagnolo P, Mazzeo PL (2009) A semi-automatic system for ground truth generation of soccer video sequences. In: 2009 Sixth IEEE international conference on advanced video and signal based surveillance, pp 559\u2013564. https:\/\/doi.org\/10.1109\/AVSS.2009.69","DOI":"10.1109\/AVSS.2009.69"},{"key":"10934_CR59","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16x16 words: transformers for image recognition at scale. CoRR abs\/2010.11929. arXiv: 2010.11929"},{"key":"10934_CR60","unstructured":"Fan H, Xiong B, Mangalam K, Li Y, Yan Z, Malik J, Feichtenhofer C (2021) Multiscale vision transformers. CoRR abs\/2104.11227, 6824\u20136835. arXiv: 2104.11227"},{"key":"10934_CR61","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C (2020) X3D: expanding architectures for efficient video recognition. CoRR abs\/2004.04730, 203\u2013213. arXiv: 2004.04730","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"10934_CR62","unstructured":"Feichtenhofer C, Fan H, Malik J, He K (2018) SlowFast networks for video recognition. CoRR abs\/1812.03982, 6202\u20136211. arXiv: 1812.03982"},{"key":"10934_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.06.070","volume":"138","author":"S Feng","year":"2019","unstructured":"Feng S, Duarte MF (2019) Few-shot learning-based human activity recognition. Expert Syst Appl 138:112782. https:\/\/doi.org\/10.1016\/j.eswa.2019.06.070","journal-title":"Expert Syst Appl"},{"issue":"39","key":"10934_CR64","doi-asserted-by":"publisher","first-page":"28971","DOI":"10.1007\/s11042-020-09414-3","volume":"79","author":"N Feng","year":"2020","unstructured":"Feng N, Song Z, Yu J, Chen Y-PP, Zhao Y, He Y, Guan T (2020) SSET: a dataset for shot segmentation, event detection, player tracking in soccer videos. Multimed Tools Appl 79(39):28971\u201328992. https:\/\/doi.org\/10.1007\/s11042-020-09414-3","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"10934_CR65","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381\u2013395. https:\/\/doi.org\/10.1145\/358669.358692","journal-title":"Commun ACM"},{"key":"10934_CR66","unstructured":"Francia S, Calderara S, Lanzi DF (2018) Classificazione di azioni cestistiche mediante tecniche di deep learning. https:\/\/www.researchgate.net\/publication\/330534530_Classificazione_di_Azioni_Cestistiche_mediante_Tecniche_di_Deep_Learning"},{"issue":"4","key":"10934_CR67","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193\u2013202. https:\/\/doi.org\/10.1007\/BF00344251","journal-title":"Biol Cybern"},{"key":"10934_CR68","doi-asserted-by":"publisher","unstructured":"Gabrielli M, Leo P, Renzi F, Bergamaschi S (2019) Action recognition to estimate Activities of Daily Living (ADL) of elderly people. In: 2019 IEEE 23rd international symposium on consumer technologies (ISCT), pp 261\u2013264. https:\/\/doi.org\/10.1109\/ISCE.2019.8900995","DOI":"10.1109\/ISCE.2019.8900995"},{"key":"10934_CR69","doi-asserted-by":"publisher","unstructured":"Gadzicki K, Khamsehashari R, Zetzsche C (2020) Early vs late fusion in multimodal convolutional neural networks. In: 2020 IEEE 23rd international conference on information fusion (FUSION), pp 1\u20136. https:\/\/doi.org\/10.23919\/FUSION45008.2020.9190246","DOI":"10.23919\/FUSION45008.2020.9190246"},{"issue":"13s","key":"10934_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3587931","volume":"55","author":"H Gammulle","year":"2023","unstructured":"Gammulle H, Ahmedt-Aristizabal D, Denman S, Tychsen-Smith L, Petersson L, Fookes C (2023) Continuous human action recognition for human-machine interaction: a review. ACM Comput Surv 55(13s):1\u201338. https:\/\/doi.org\/10.1145\/3587931","journal-title":"ACM Comput Surv"},{"key":"10934_CR71","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-20518-8_12","volume-title":"Advances in computational intelligence","author":"Y Ganesh","year":"2019","unstructured":"Ganesh Y, Sri Teja A, Munnangi SK, Rama Murthy G (2019) A novel framework for fine grained action recognition in soccer. In: Rojas I, Joya G, Catala A (eds) Advances in computational intelligence, vol 11507. Springer, Cham, pp 137\u2013150"},{"key":"10934_CR72","doi-asserted-by":"publisher","unstructured":"Gao X, Liu X, Yang T, Deng G, Peng H, Zhang Q, Li H, Liu J (2020) Automatic key moment extraction and highlights generation based on comprehensive soccer video understanding. In: 2020 IEEE international conference on multimedia & expo workshops (ICMEW), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICMEW46912.2020.9106051","DOI":"10.1109\/ICMEW46912.2020.9106051"},{"key":"10934_CR73","doi-asserted-by":"publisher","unstructured":"Gemmeke JF, Ellis DPW, Freedman D, Jansen A, Lawrence W, Moore RC, Plakal M, Ritter M (2017) Audio Set: an ontology and human-labeled dataset for audio events. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 776\u2013780. https:\/\/doi.org\/10.1109\/ICASSP.2017.7952261","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"10934_CR75","unstructured":"Giancola S, Ghanem B (2021) Temporally-aware feature pooling for action spotting in soccer broadcasts. CoRR abs\/2104.06779, 4490\u20134499. arXiv: 2104.06779"},{"key":"10934_CR74","doi-asserted-by":"crossref","unstructured":"Giancola S, Amine M, Dghaily T, Ghanem B (2018) SoccerNet: a scalable dataset for action spotting in soccer videos. CoRR abs\/1804.04527, 1711\u20131721. arXiv: 1804.04527","DOI":"10.1109\/CVPRW.2018.00223"},{"key":"10934_CR77","unstructured":"Girdhar R, Singh M, Ravi N, Maaten Lvd, Joulin A, Misra I (2022) Omnivore: a single model for many visual modalities. CoRR abs\/2201.08377, 16102\u201316112. arXiv: 2201.08377"},{"key":"10934_CR76","doi-asserted-by":"crossref","unstructured":"Girdhar R, El-Nouby A, Singh M, Alwala KV, Joulin A, Misra I (2023) OmniMAE: single model masked pretraining on images and videos","DOI":"10.1109\/CVPR52729.2023.01003"},{"key":"10934_CR78","doi-asserted-by":"crossref","unstructured":"Gong Y, Chung Y-A, Glass JR (2021) AST: audio spectrogram transformer. CoRR abs\/2104.01778. arXiv: 2104.01778","DOI":"10.21437\/Interspeech.2021-698"},{"key":"10934_CR79","doi-asserted-by":"crossref","unstructured":"Goyal R, Kahou SE, Michalski V, Materzynska J, Westphal S, Kim H, Haenel V, Fr\u00fcnd I, Yianilos P, Mueller-Freitag M, Hoppe F, Thurau C, Bax I, Memisevic R (2017) The \u201csomething something\u201d video database for learning and evaluating visual common sense. CoRR abs\/1706.04261, 5842\u20135850. arXiv: 1706.04261","DOI":"10.1109\/ICCV.2017.622"},{"issue":"8","key":"10934_CR80","doi-asserted-by":"publisher","first-page":"1576","DOI":"10.1109\/TPAMI.2011.253","volume":"34","author":"T Guha","year":"2012","unstructured":"Guha T, Ward RK (2012) Learning sparse representations for human action recognition. IEEE Trans Pattern Anal Mach Intell 34(8):1576\u20131588. https:\/\/doi.org\/10.1109\/TPAMI.2011.253","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"10934_CR81","doi-asserted-by":"publisher","first-page":"4755","DOI":"10.1007\/s10462-021-10116-x","volume":"55","author":"N Gupta","year":"2022","unstructured":"Gupta N, Gupta SK, Pathak RK, Jain V, Rashidi P, Suri JS (2022) Human activity recognition in artificial intelligence framework: a narrative review. Artif Intell Rev 55(6):4755\u20134808. https:\/\/doi.org\/10.1007\/s10462-021-10116-x","journal-title":"Artif Intell Rev"},{"key":"10934_CR82","unstructured":"Gu C, Sun C, Vijayanarasimhan S, Pantofaru C, Ross DA, Toderici G, Li Y, Ricco S, Sukthankar R, Schmid C, Malik J (2017) AVA: a video dataset of spatio-temporally localized atomic visual actions. CoRR abs\/1705.08421, 6047\u20136056. arXiv: 1705.08421"},{"key":"10934_CR83","doi-asserted-by":"publisher","unstructured":"Gu X, Xue X, Wang F (2020) Fine-grained action recognition on a novel basketball dataset. In: ICASSP 2020\u20142020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2563\u20132567. IEEE, Barcelona, Spain. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053928. Accessed 12 Dec 2023","DOI":"10.1109\/ICASSP40776.2020.9053928"},{"issue":"1","key":"10934_CR84","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11263-016-0917-2","volume":"121","author":"S Hadfield","year":"2017","unstructured":"Hadfield S, Lebeda K, Bowden R (2017) Hollywood 3D: what are the best 3D features for action recognition? Int J Comput Vis 121(1):95\u2013110. https:\/\/doi.org\/10.1007\/s11263-016-0917-2","journal-title":"Int J Comput Vis"},{"issue":"3","key":"10934_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550311","volume":"6","author":"J He","year":"2022","unstructured":"He J, Yang W (2022) IMar: multi-user continuous action recognition with WiFi signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 6(3):1\u201327. https:\/\/doi.org\/10.1145\/3550311","journal-title":"Proc ACM Interact Mob Wearable Ubiquitous Technol"},{"key":"10934_CR86","doi-asserted-by":"publisher","unstructured":"Heilbron, F.C, Escorcia, V, Ghanem, B, Niebles, J.C.: ActivityNet: a large-scale video benchmark for human activity understanding. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 961\u2013970 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298698","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"10934_CR87","doi-asserted-by":"crossref","unstructured":"Held J, Cioppa A, Giancola S, Hamdi A, Ghanem B, Van\u00a0Droogenbroeck M (2023) Vars: video assistant referee system for automated soccer decision making from multiple views. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops, pp 5086\u20135097","DOI":"10.1109\/CVPRW59228.2023.00537"},{"key":"10934_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2017.01.010","volume-title":"Going deeper into action recognition: a survey","author":"S Herath","year":"2017","unstructured":"Herath S, Harandi M, Porikli F (2017) Going deeper into action recognition: a survey. Elsevier, Amsterdam"},{"key":"10934_CR89","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network"},{"issue":"8","key":"10934_CR90","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"10934_CR91","doi-asserted-by":"crossref","unstructured":"Hoelzemann A, Romero JL, Bock M, Laerhoven KV, Lv Q (2023) Hang-time HAR: a benchmark dataset for basketball activity recognition using wrist-worn inertial sensors. MDPI","DOI":"10.3390\/s23135879"},{"key":"10934_CR92","doi-asserted-by":"crossref","unstructured":"Hong J, Fisher M, Gharbi M, Fatahalian K (2021) Video pose distillation for few-shot, fine-grained sports action recognition. CoRR abs\/2109.01305, 9254\u20139263. arXiv: 2109.01305","DOI":"10.1109\/ICCV48922.2021.00912"},{"issue":"6","key":"10934_CR93","doi-asserted-by":"publisher","first-page":"09633","DOI":"10.1016\/j.heliyon.2022.e09633","volume":"8","author":"K Host","year":"2022","unstructured":"Host K, Iva\u0161ic-Kos M (2022) An overview of human action recognition in sports based on computer vision. Heliyon 8(6):09633. https:\/\/doi.org\/10.1016\/j.heliyon.2022.e09633","journal-title":"Heliyon"},{"key":"10934_CR95","doi-asserted-by":"publisher","unstructured":"Hu L, Huang S, Wang S, Liu W, Ning J (2021) Do we really need frame-by-frame annotation datasets for object tracking? In: Proceedings of the 29th ACM international conference on multimedia. MM \u201921. Association for Computing Machinery, New York, NY, USA, pp 4949\u20134957. Event-place: Virtual Event, China. https:\/\/doi.org\/10.1145\/3474085.3475365","DOI":"10.1145\/3474085.3475365"},{"issue":"3","key":"10934_CR94","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s10462-022-10210-8","volume":"56","author":"K Hu","year":"2023","unstructured":"Hu K, Jin J, Zheng F, Weng L, Ding Y (2023) Overview of behavior recognition based on deep learning. Artif Intell Rev 56(3):1833\u20131865. https:\/\/doi.org\/10.1007\/s10462-022-10210-8","journal-title":"Artif Intell Rev"},{"key":"10934_CR96","doi-asserted-by":"publisher","unstructured":"Huiqun Z, Hui W, Xiaoling W (2011) Application research of video annotation in sports video analysis. In: 2011 international conference on future computer science and education, pp 62\u201366. https:\/\/doi.org\/10.1109\/ICFCSE.2011.24","DOI":"10.1109\/ICFCSE.2011.24"},{"key":"10934_CR97","doi-asserted-by":"crossref","unstructured":"Idrees H, Zamir AR, Jiang Y-G, Gorban A, Laptev I, Sukthankar R, Shah M (2016) The THUMOS challenge on action recognition for videos \"in the Wild\". CoRR 155, 1\u201323. arXiv: 1604.06182","DOI":"10.1016\/j.cviu.2016.10.018"},{"key":"10934_CR98","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106060","volume":"149","author":"MM Islam","year":"2022","unstructured":"Islam MM, Nooruddin S, Karray F, Muhammad G (2022) Human activity recognition using tools of convolutional neural networks: a state of the art review, data sets, challenges, and future prospects. Comput Biol Med 149:106060. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106060","journal-title":"Comput Biol Med"},{"key":"10934_CR99","unstructured":"Isola P, Zhu J-Y, Zhou T, Efros AA (2016) Image-to-image translation with conditional adversarial networks. CoRR abs\/1611.07004, 1125\u20131134. arXiv: 1611.07004"},{"issue":"4","key":"10934_CR100","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1007\/s13042-021-01441-2","volume":"13","author":"MH Javed","year":"2022","unstructured":"Javed MH, Yu Z, Li T, Rajeh TM, Rafique F, Waqar S (2022) Hybrid two-stream dynamic CNN for view adaptive human action recognition using ensemble learning. Int J Mach Learn Cybern 13(4):1157\u20131166. https:\/\/doi.org\/10.1007\/s13042-021-01441-2","journal-title":"Int J Mach Learn Cybern"},{"key":"10934_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsidi.2019.200901","volume":"32","author":"I Jegham","year":"2020","unstructured":"Jegham I, Khalifa AB, Alouani I, Mahjoub MA (2020) Vision-based human action recognition: an overview and real world challenges. Forensic Sci Int 32:200901. https:\/\/doi.org\/10.1016\/j.fsidi.2019.200901","journal-title":"Forensic Sci Int"},{"issue":"1","key":"10934_CR102","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231. https:\/\/doi.org\/10.1109\/TPAMI.2012.59","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10934_CR105","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick RB, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. CoRR abs\/1408.5093, 675\u2013678. arXiv: 1408.5093","DOI":"10.1145\/2647868.2654889"},{"key":"10934_CR104","unstructured":"Jiang Y, Cui K, Chen L, Wang C, Wang C, Liu H, Xu C (2019) Comprehensive soccer video understanding: towards human-comparable video understanding system in constrained environment. CoRR abs\/1912.04465. arXiv: 1912.04465"},{"key":"10934_CR103","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","volume":"199","author":"P Jiang","year":"2022","unstructured":"Jiang P, Ergu D, Liu F, Cai Y, Ma B (2022) A review of Yolo algorithm developments. Procedia Comput Sci 199:1066\u20131073. https:\/\/doi.org\/10.1016\/j.procs.2022.01.135","journal-title":"Procedia Comput Sci"},{"key":"10934_CR106","unstructured":"Karimi A, Toosi R, Akhaee MA (2021) Soccer event detection using deep learning. CoRR abs\/2102.04331. arXiv: 2102.04331"},{"key":"10934_CR107","doi-asserted-by":"publisher","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: 2014 IEEE conference on computer vision and pattern recognition, pp 1725\u20131732. https:\/\/doi.org\/10.1109\/CVPR.2014.223","DOI":"10.1109\/CVPR.2014.223"},{"key":"10934_CR108","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, Suleyman M, Zisserman A (2017) The kinetics human action video dataset. CoRR abs\/1705.06950. arXiv: 1705.06950"},{"key":"10934_CR109","doi-asserted-by":"crossref","unstructured":"Kazakos E, Nagrani A, Zisserman A, Damen D (2019) Epic-fusion: audio-visual temporal binding for egocentric action recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 5492\u20135501","DOI":"10.1109\/ICCV.2019.00559"},{"key":"10934_CR110","doi-asserted-by":"crossref","unstructured":"Khan SH, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2021) Transformers in vision: a survey. CoRR 54, 1\u201341. arXiv: 2101.01169","DOI":"10.1145\/3505244"},{"issue":"3","key":"10934_CR111","doi-asserted-by":"publisher","first-page":"3528","DOI":"10.1007\/s11227-023-05611-7","volume":"80","author":"SB Khobdeh","year":"2023","unstructured":"Khobdeh SB, Yamaghani MR, Sareshkeh SK (2023) Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network. J Supercomput 80(3):3528\u20133553. https:\/\/doi.org\/10.1007\/s11227-023-05611-7","journal-title":"J Supercomput"},{"issue":"7","key":"10934_CR112","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1136\/bjsports-2019-101344","volume":"55","author":"C Klein","year":"2021","unstructured":"Klein C, Luig P, Henke T, Bloch H, Platen P (2021) Nine typical injury patterns in German professional male football (soccer): a systematic visual video analysis of 345 match injuries. Br J Sports Med 55(7):390\u2013396. https:\/\/doi.org\/10.1136\/bjsports-2019-101344","journal-title":"Br J Sports Med"},{"issue":"5","key":"10934_CR113","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1007\/s11263-022-01594-9","volume":"130","author":"Y Kong","year":"2022","unstructured":"Kong Y, Fu Y (2022) Human action recognition and prediction: a survey. Int J Comput Vis 130(5):1366\u20131401. https:\/\/doi.org\/10.1007\/s11263-022-01594-9","journal-title":"Int J Comput Vis"},{"key":"10934_CR114","doi-asserted-by":"publisher","unstructured":"Kong Y, Zhang X, Wei Q, Hu W, Jia Y (2008) Group action recognition in soccer videos. In: 2008 19th international conference on pattern recognition, pp 1\u20134. https:\/\/doi.org\/10.1109\/ICPR.2008.4761001","DOI":"10.1109\/ICPR.2008.4761001"},{"key":"10934_CR115","doi-asserted-by":"crossref","unstructured":"Kotseruba I, Rasouli A, Tsotsos JK (2021) Benchmark for evaluating pedestrian action prediction. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV), pp 1258\u20131268","DOI":"10.1109\/WACV48630.2021.00130"},{"issue":"2","key":"10934_CR116","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1162\/089976603762552951","volume":"15","author":"K Kreutz-Delgado","year":"2003","unstructured":"Kreutz-Delgado K, Murray JF, Rao BD, Engan K, Lee T-W, Sejnowski TJ (2003) Dictionary learning algorithms for sparse representation. Neural Comput 15(2):349\u2013396. https:\/\/doi.org\/10.1162\/089976603762552951","journal-title":"Neural Comput"},{"key":"10934_CR118","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25"},{"issue":"6","key":"10934_CR117","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"10934_CR119","doi-asserted-by":"publisher","unstructured":"Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) Hmdb: a large video database for human motion recognition. In: 2011 international conference on computer vision, pp 2556\u20132563. https:\/\/doi.org\/10.1109\/ICCV.2011.6126543","DOI":"10.1109\/ICCV.2011.6126543"},{"issue":"21","key":"10934_CR120","doi-asserted-by":"publisher","first-page":"18289","DOI":"10.1007\/s00521-022-07665-9","volume":"34","author":"F Kulsoom","year":"2022","unstructured":"Kulsoom F, Narejo S, Mehmood Z, Chaudhry HN, Butt A, Bashir AK (2022) A review of machine learning-based human activity recognition for diverse applications. Neural Comput Appl 34(21):18289\u201318324. https:\/\/doi.org\/10.1007\/s00521-022-07665-9","journal-title":"Neural Comput Appl"},{"key":"10934_CR121","doi-asserted-by":"crossref","unstructured":"Kumar\u00a0Dwivedi S, Gupta V, Mitra R, Ahmed S, Jain A (2019) Protogan: towards few shot learning for action recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) workshops","DOI":"10.1109\/ICCVW.2019.00166"},{"key":"10934_CR122","doi-asserted-by":"publisher","first-page":"86339","DOI":"10.1109\/ACCESS.2022.3198985","volume":"10","author":"KB Kwan-Loo","year":"2022","unstructured":"Kwan-Loo KB, Ort\u00edz-Bayliss JC, Conant-Pablos SE, Terashima-Mar\u00edn H, Rad P (2022) Detection of violent behavior using neural networks and pose estimation. IEEE Access 10:86339\u201386352. https:\/\/doi.org\/10.1109\/ACCESS.2022.3198985","journal-title":"IEEE Access"},{"issue":"2","key":"10934_CR124","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2):107\u2013123. https:\/\/doi.org\/10.1007\/s11263-005-1838-7","journal-title":"Int J Comput Vis"},{"key":"10934_CR125","doi-asserted-by":"publisher","unstructured":"Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1\u20138. https:\/\/doi.org\/10.1109\/CVPR.2008.4587756","DOI":"10.1109\/CVPR.2008.4587756"},{"key":"10934_CR123","doi-asserted-by":"publisher","unstructured":"Laptev Lindeberg (2003) Space-time interest points. In: Proceedings Ninth IEEE international conference on computer vision, vol 1, pp 432\u20134391. https:\/\/doi.org\/10.1109\/ICCV.2003.1238378","DOI":"10.1109\/ICCV.2003.1238378"},{"key":"10934_CR126","unstructured":"Larsen AG, Papi G (2023) Prediction of football actions and identification of optimal sensor placements using a semi-supervised learning approach"},{"issue":"1","key":"10934_CR127","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/925\/1\/012026","volume":"925","author":"BM Latha","year":"2020","unstructured":"Latha BM, Manjula BK, Sumana CV, Hemalatha KL (2020) Human action recognition using STIP evaluation techniques. IOP Conf Ser 925(1):012026. https:\/\/doi.org\/10.1088\/1757-899X\/925\/1\/012026","journal-title":"IOP Conf Ser"},{"issue":"3","key":"10934_CR128","doi-asserted-by":"publisher","first-page":"866","DOI":"10.3390\/s22030866","volume":"22","author":"M Leo","year":"2022","unstructured":"Leo M, Bernava GM, Carcagn\u00ec P, Distante C (2022) Video-based automatic baby motion analysis for early neurological disorder diagnosis: state of the art and future directions. Sensors 22(3):866. https:\/\/doi.org\/10.3390\/s22030866","journal-title":"Sensors"},{"key":"10934_CR129","doi-asserted-by":"publisher","unstructured":"Leslie WHP (1973) Numerical control: Mathematics and applications, P. B\u00e9zier, Wiley, London, 1972. No of pages: 240. Price: \u00a34.95. Int J Numer Methods Eng 6(3):456. https:\/\/doi.org\/10.1002\/nme.1620060322","DOI":"10.1002\/nme.1620060322"},{"issue":"7","key":"10934_CR130","doi-asserted-by":"publisher","first-page":"54","DOI":"10.12775\/JEHS.2021.11.07.005","volume":"11","author":"B Li","year":"2021","unstructured":"Li B, Xu X (2021) Application of artificial intelligence in basketball sport. J Educ Health Sport 11(7):54\u201367. https:\/\/doi.org\/10.12775\/JEHS.2021.11.07.005","journal-title":"J Educ Health Sport"},{"key":"10934_CR149","doi-asserted-by":"publisher","unstructured":"Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3D points. In: 2010 IEEE computer society conference on computer vision and pattern recognition\u2014workshops, pp 9\u201314. https:\/\/doi.org\/10.1109\/CVPRW.2010.5543273","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"10934_CR136","unstructured":"Li A, Thotakuri M, Ross DA, Carreira J, Vostrikov A, Zisserman A (2020) The AVA-kinetics localized human actions video dataset. CoRR abs\/2005.00214. arXiv: 2005.00214"},{"key":"10934_CR133","unstructured":"Li Y, Chen L, He R, Wang Z, Wu G, Wang L (2021) MultiSports: a multi-person video dataset of spatio-temporally localized sports actions. CoRR abs\/2105.07404, 13536\u201313545. arXiv: 2105.07404"},{"key":"10934_CR148","doi-asserted-by":"crossref","unstructured":"Li Y, Wu C-Y, Fan H, Mangalam K, Xiong B, Malik J, Feichtenhofer C (2022) Mvitv2: improved multiscale vision transformers for classification and detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4804\u20134814","DOI":"10.1109\/CVPR52688.2022.00476"},{"key":"10934_CR134","unstructured":"Li H, Deng A, Ke Q, Liu J, Rahmani H, Guo Y, Schiele B, Chen C (2024) Sports-qa: A large-scale video question answering benchmark for complex and professional sports. arXiv preprint arXiv:2401.01505"},{"issue":"2","key":"10934_CR131","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.3934\/mbe.2023088","volume":"20","author":"X Liang","year":"2022","unstructured":"Liang X (2022) A video images-aware knowledge extraction method for intelligent healthcare management of basketball players. Math Biosci Eng 20(2):1919\u20131937. https:\/\/doi.org\/10.3934\/mbe.2023088","journal-title":"Math Biosci Eng"},{"key":"10934_CR132","doi-asserted-by":"crossref","unstructured":"Liberatori B, Conti A, Rota P, Wang Y, Ricci E (2024) Test-time zero-shot temporal action localization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 18720\u201318729","DOI":"10.1109\/CVPR52733.2024.01771"},{"key":"10934_CR135","unstructured":"Lin J, Gan C, Han S (2018) Temporal shift module for efficient video understanding. CoRR abs\/1811.08383. arXiv: 1811.08383"},{"key":"10934_CR142","doi-asserted-by":"publisher","unstructured":"Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos \u201cin the wild\u201d. In: 2009 IEEE conference on computer vision and pattern recognition, pp 1996\u20132003. https:\/\/doi.org\/10.1109\/CVPR.2009.5206744","DOI":"10.1109\/CVPR.2009.5206744"},{"key":"10934_CR145","doi-asserted-by":"crossref","unstructured":"Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal lstm with trust gates for 3d human action recognition","DOI":"10.1007\/978-3-319-46487-9_50"},{"issue":"2","key":"10934_CR137","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s11280-018-0642-6","volume":"22","author":"K Liu","year":"2019","unstructured":"Liu K, Liu W, Ma H, Huang W, Dong X (2019a) Generalized zero-shot learning for action recognition with web-scale video data. World Wide Web 22(2):807\u2013824. https:\/\/doi.org\/10.1007\/s11280-018-0642-6","journal-title":"World Wide Web"},{"key":"10934_CR144","doi-asserted-by":"crossref","unstructured":"Liu J, Shahroudy A, Perez M, Wang G, Duan L-Y, Kot AC (2019b) NTU RGB+D 120: a large-scale benchmark for 3D human activity understanding. CoRR 42(10):2684\u20132701. arXiv: 1905.04757","DOI":"10.1109\/TPAMI.2019.2916873"},{"key":"10934_CR147","unstructured":"Liu Z, Wang L, Wu W, Qian C, Lu T (2020) TAM: temporal adaptive module for video recognition. CoRR abs\/2005.06803, 13708\u201313718. arXiv: 2005.06803"},{"key":"10934_CR141","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021a) Swin transformer: hierarchical vision transformer using shifted windows. CoRR abs\/2103.14030, 10012\u201310022. arXiv: 2103.14030"},{"key":"10934_CR143","unstructured":"Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H (2021b) Video swin transformer. CoRR abs\/2106.13230, 3202\u20133211. arXiv: 2106.13230"},{"key":"10934_CR146","doi-asserted-by":"crossref","unstructured":"Liu X, Wang Q, Hu Y, Tang X, Bai S, Bai X (2021c) End-to-end temporal action detection with transformer. CoRR 31:5427\u20135441. arXiv: 2106.10271","DOI":"10.1109\/TIP.2022.3195321"},{"key":"10934_CR138","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.neucom.2022.06.034","volume":"501","author":"F Liu","year":"2022","unstructured":"Liu F, Xu X, Xing X, Guo K, Wang L (2022a) Simple-action-guided dictionary learning for complex action recognition. Neurocomputing 501:387\u2013396. https:\/\/doi.org\/10.1016\/j.neucom.2022.06.034","journal-title":"Neurocomputing"},{"key":"10934_CR140","doi-asserted-by":"crossref","unstructured":"Liu Z, Hu H, Lin Y, Yao Z, Xie Z, Wei Y, Ning J, Cao Y, Zhang Z, Dong L (2022b) Swin transformer v2: scaling up capacity and resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12009\u201312019","DOI":"10.1109\/CVPR52688.2022.01170"},{"issue":"9","key":"10934_CR139","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3556537","volume":"55","author":"M Liu","year":"2023","unstructured":"Liu M, Nie L, Wang Y, Wang M, Rui Y (2023) A survey on video moment localization. ACM Comput Surv 55(9):1\u201337. https:\/\/doi.org\/10.1145\/3556537","journal-title":"ACM Comput Surv"},{"issue":"12","key":"10934_CR150","doi-asserted-by":"publisher","first-page":"155014771989453","DOI":"10.1177\/1550147719894532","volume":"15","author":"IH Lopez-Nava","year":"2019","unstructured":"Lopez-Nava IH, Mu\u00f1oz-Mel\u00e9ndez A (2019) Human action recognition based on low- and high-level data from wearable inertial sensors. Int J Distrib Sens Netw 15(12):1550147719894532","journal-title":"Int J Distrib Sens Netw"},{"key":"10934_CR151","doi-asserted-by":"publisher","unstructured":"Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2, pp 1150\u20131157. https:\/\/doi.org\/10.1109\/ICCV.1999.790410","DOI":"10.1109\/ICCV.1999.790410"},{"issue":"2","key":"10934_CR152","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91\u2013110. https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int J Comput Vis"},{"key":"10934_CR154","doi-asserted-by":"crossref","unstructured":"Lu K, Chen J, Little JJ, He H (2017) Light cascaded convolutional neural networks for accurate player detection. CoRR abs\/1709.10230. arXiv: 1709.10230","DOI":"10.5244\/C.31.173"},{"key":"10934_CR153","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/978-3-031-08757-8_47","volume-title":"Computational Science\u2014ICCS 2022","author":"SK Luan","year":"2022","unstructured":"Luan SK, Yin H, Sinnott R (2022) Action recognition in Australian rules football through deep learning. In: Groen D, Mulatier C, Paszynski M, Krzhizhanovskaya VV, Dongarra JJ, Sloot PMA (eds) Computational Science\u2014ICCS 2022, vol 13352. Springer, Cham, pp 563\u2013576"},{"key":"10934_CR160","doi-asserted-by":"publisher","unstructured":"Ma S, Shao E, Xie X, Liu W (2020) Event detection in soccer video based on self-attention. In: 2020 IEEE 6th international conference on computer and communications (ICCC). IEEE, Chengdu, China, pp 1852\u20131856. https:\/\/doi.org\/10.1109\/ICCC51575.2020.9344896. Accessed 27 Nov 2023","DOI":"10.1109\/ICCC51575.2020.9344896"},{"issue":"10","key":"10934_CR155","doi-asserted-by":"publisher","first-page":"4426","DOI":"10.3390\/app11104426","volume":"11","author":"C Ma","year":"2021","unstructured":"Ma C, Fan J, Yao J, Zhang T (2021) NPU RGBD dataset and a feature-enhanced LSTM-DGCN method for action recognition of basketball players+. Appl Sci 11(10):4426. https:\/\/doi.org\/10.3390\/app11104426","journal-title":"Appl Sci"},{"key":"10934_CR156","doi-asserted-by":"publisher","unstructured":"Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th annual international conference on machine learning. ICML \u201909. Association for Computing Machinery, New York, NY, USA. event-place: Montreal, Quebec, Canada, pp 689\u2013696. https:\/\/doi.org\/10.1145\/1553374.1553463","DOI":"10.1145\/1553374.1553463"},{"issue":"12","key":"10934_CR157","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1109\/78.258082","volume":"41","author":"SG Mallat","year":"1993","unstructured":"Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397\u20133415. https:\/\/doi.org\/10.1109\/78.258082","journal-title":"IEEE Trans Signal Process"},{"issue":"12","key":"10934_CR158","doi-asserted-by":"publisher","first-page":"18693","DOI":"10.1007\/s11042-021-10570-3","volume":"80","author":"R Maqsood","year":"2021","unstructured":"Maqsood R, Bajwa UI, Saleem G, Raza RH, Anwar MW (2021) Anomaly recognition from surveillance videos using 3D convolution neural network. Multimed Tools Appl 80(12):18693\u201318716. https:\/\/doi.org\/10.1007\/s11042-021-10570-3","journal-title":"Multimed Tools Appl"},{"key":"10934_CR159","doi-asserted-by":"publisher","unstructured":"Marszalek M, Laptev I, Schmid C (2009) Actions in context. In: 2009 IEEE conference on computer vision and pattern recognition, pp 2929\u20132936. https:\/\/doi.org\/10.1109\/CVPR.2009.5206557","DOI":"10.1109\/CVPR.2009.5206557"},{"key":"10934_CR162","doi-asserted-by":"publisher","unstructured":"Mishra A, Verma VK, Reddy MSK, S A, Rai P, Mittal A (2018) A generative approach to zero-shot and few-shot action recognition. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 372\u2013380. https:\/\/doi.org\/10.1109\/WACV.2018.00047","DOI":"10.1109\/WACV.2018.00047"},{"key":"10934_CR161","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neucom.2020.01.078","volume":"390","author":"A Mishra","year":"2020","unstructured":"Mishra A, Pandey A, Murthy HA (2020) Zero-shot learning for action recognition using synthesized features. Neurocomputing 390:117\u2013130. https:\/\/doi.org\/10.1016\/j.neucom.2020.01.078","journal-title":"Neurocomputing"},{"key":"10934_CR163","doi-asserted-by":"crossref","unstructured":"Mkhallati H, Cioppa A, Giancola S, Ghanem B, Van\u00a0Droogenbroeck M (2023) Soccernet-caption: dense video captioning for soccer broadcasts commentaries. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops, pp 5074\u20135085","DOI":"10.1109\/CVPRW59228.2023.00536"},{"key":"10934_CR164","unstructured":"Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. CoRR 27. arXiv: 1406.6247"},{"key":"10934_CR166","doi-asserted-by":"crossref","unstructured":"Monfort M, Zhou B, Bargal SA, Andonian A, Yan T, Ramakrishnan K, Brown LM, Fan Q, Gutfreund D, Vondrick C, Oliva A (2018) Moments in time dataset: one million videos for event understanding. CoRR abs\/1801.03150, 502\u2013508. arXiv: 1801.03150","DOI":"10.1109\/TPAMI.2019.2901464"},{"key":"10934_CR165","doi-asserted-by":"crossref","unstructured":"Monfort M, Ramakrishnan K, Andonian A, McNamara BA, Lascelles A, Pan B, Fan Q, Gutfreund D, Feris RS, Oliva A (2019) Multi-moments in time: learning and interpreting models for multi-action video understanding. CoRR 44(12):9434\u20139445. arXiv: 1911.00232","DOI":"10.1109\/TPAMI.2021.3126682"},{"key":"10934_CR167","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.humov.2015.03.007","volume":"41","author":"R Montoliu","year":"2015","unstructured":"Montoliu R, Mart\u00edn-F\u00e9lez R, Torres-Sospedra J, Mart\u00ednez-Us\u00f3 A (2015) Team activity recognition in Association Football using a Bag-of-Words-based method. Hum Mov Sci 41:165\u2013178. https:\/\/doi.org\/10.1016\/j.humov.2015.03.007","journal-title":"Hum Mov Sci"},{"issue":"4","key":"10934_CR168","doi-asserted-by":"publisher","first-page":"2182","DOI":"10.3390\/s23042182","volume":"23","author":"MG Morshed","year":"2023","unstructured":"Morshed MG, Sultana T, Alam A, Lee Y-K (2023) Human action recognition: a taxonomy-based survey, updates, and opportunities. Sensors 23(4):2182. https:\/\/doi.org\/10.3390\/s23042182","journal-title":"Sensors"},{"issue":"9","key":"10934_CR169","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.3390\/app12094429","volume":"12","author":"BT Naik","year":"2022","unstructured":"Naik BT, Hashmi MF, Bokde ND (2022) A comprehensive review of computer vision in sports: open issues, future trends and research directions. Appl Sci 12(9):4429. https:\/\/doi.org\/10.3390\/app12094429","journal-title":"Appl Sci"},{"key":"10934_CR170","doi-asserted-by":"crossref","unstructured":"Ng JY-H, Hausknecht MJ, Vijayanarasimhan S, Vinyals O, Monga R, Toderici G (2015) Beyond short snippets: deep networks for video classification. CoRR abs\/1503.08909, 4694\u20134702. arXiv: 1503.08909","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"10934_CR171","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/978-3-642-15552-9_29","volume-title":"Computer vision\u2014ECCV 2010","author":"JC Niebles","year":"2010","unstructured":"Niebles JC, Chen C-W, Fei-Fei L (2010) Modeling temporal structure of decomposable motion segments for activity classification. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision\u2014ECCV 2010, vol 6312. Springer, Berlin, pp 392\u2013405"},{"key":"10934_CR172","doi-asserted-by":"crossref","unstructured":"Noor N, Park IK (2023) A lightweight skeleton-based 3d-cnn for real-time fall detection and action recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) workshops, pp 2179\u20132188","DOI":"10.1109\/ICCVW60793.2023.00232"},{"issue":"23","key":"10934_CR173","doi-asserted-by":"publisher","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","volume":"37","author":"BA Olshausen","year":"1997","unstructured":"Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis Res 37(23):3311\u20133325. https:\/\/doi.org\/10.1016\/S0042-6989(97)00169-7","journal-title":"Vis Res"},{"key":"10934_CR174","doi-asserted-by":"publisher","first-page":"920","DOI":"10.7717\/peerj-cs.920","volume":"8","author":"B Omarov","year":"2022","unstructured":"Omarov B, Narynov S, Zhumanov Z, Gumar A, Khassanova M (2022) State-of-the-art violence detection techniques in video surveillance security systems: a systematic review. PeerJ Comput Sci 8:920. https:\/\/doi.org\/10.7717\/peerj-cs.920","journal-title":"PeerJ Comput Sci"},{"key":"10934_CR175","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2020.115784","volume":"83","author":"Z Pan","year":"2020","unstructured":"Pan Z, Li C (2020) Robust basketball sports recognition by leveraging motion block estimation. Signal Process 83:115784. https:\/\/doi.org\/10.1016\/j.image.2020.115784","journal-title":"Signal Process"},{"issue":"1","key":"10934_CR176","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41597-019-0247-7","volume":"6","author":"L Pappalardo","year":"2019","unstructured":"Pappalardo L, Cintia P, Rossi A, Massucco E, Ferragina P, Pedreschi D, Giannotti F (2019) A public data set of spatio-temporal match events in soccer competitions. Sci Data 6(1):236. https:\/\/doi.org\/10.1038\/s41597-019-0247-7","journal-title":"Sci Data"},{"key":"10934_CR177","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.cviu.2017.01.001","volume":"159","author":"P Parisot","year":"2017","unstructured":"Parisot P, Vleeschouwer CD (2017) Scene-specific classifier for effective and efficient team sport players detection from a single calibrated camera. Comput Vis Image Underst 159:74\u201388. https:\/\/doi.org\/10.1016\/j.cviu.2017.01.001","journal-title":"Comput Vis Image Underst"},{"issue":"20","key":"10934_CR178","doi-asserted-by":"publisher","first-page":"10445","DOI":"10.3390\/app122010445","volume":"12","author":"SK Park","year":"2022","unstructured":"Park SK, Chung JH, Pae DS, Lim MT (2022) Binary dense SIFT flow based position-information added two-stream CNN for pedestrian action recognition. Appl Sci 12(20):10445. https:\/\/doi.org\/10.3390\/app122010445","journal-title":"Appl Sci"},{"key":"10934_CR179","doi-asserted-by":"crossref","unstructured":"Parmar P, Morris B (2021) Win-fail action recognition. CoRR abs\/2102.07355, 161\u2013171. arXiv: 2102.07355","DOI":"10.1109\/WACVW54805.2022.00022"},{"key":"10934_CR180","doi-asserted-by":"publisher","unstructured":"Pei M, Jia Y, Zhu S-C (2011) Parsing video events with goal inference and intent prediction. In: 2011 international conference on computer vision, pp 487\u2013494. https:\/\/doi.org\/10.1109\/ICCV.2011.6126279","DOI":"10.1109\/ICCV.2011.6126279"},{"key":"10934_CR181","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-319-10602-1_38","volume-title":"Computer vision\u2014ECCV 2014","author":"X Peng","year":"2014","unstructured":"Peng X, Zou C, Qiao Y, Peng Q (2014) Action recognition with stacked fisher vectors. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision\u2014ECCV 2014. Springer, Cham, pp 581\u2013595"},{"key":"10934_CR183","doi-asserted-by":"publisher","unstructured":"Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1\u20138. https:\/\/doi.org\/10.1109\/CVPR.2007.383266","DOI":"10.1109\/CVPR.2007.383266"},{"key":"10934_CR182","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-642-15561-1_11","volume-title":"Computer vision\u2014ECCV 2010","author":"F Perronnin","year":"2010","unstructured":"Perronnin F, S\u00e1nchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision\u2014ECCV 2010. Springer, Berlin, pp 143\u2013156"},{"key":"10934_CR184","doi-asserted-by":"crossref","unstructured":"Phan T, Vo K, Le D, Doretto G, Adjeroh D, Le N (2024) Zeetad: adapting pretrained vision-language model for zero-shot end-to-end temporal action detection. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 7046\u20137055","DOI":"10.1109\/WACV57701.2024.00689"},{"key":"10934_CR185","doi-asserted-by":"publisher","unstructured":"Pogadadanda V, Shaik S, Neeraj GVS, Siralam HV, Joseph\u00a0S IT, Rao KBVB (2023) Abnormal activity recognition on surveillance: a review. In: 2023 third international conference on artificial intelligence and smart energy (ICAIS), pp 1072\u20131077. https:\/\/doi.org\/10.1109\/ICAIS56108.2023.10073703","DOI":"10.1109\/ICAIS56108.2023.10073703"},{"issue":"3","key":"10934_CR186","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TPAMI.2011.158","volume":"34","author":"A Prest","year":"2012","unstructured":"Prest A, Schmid C, Ferrari V (2012) Weakly supervised learning of interactions between humans and objects. IEEE Trans Pattern Anal Mach Intell 34(3):601\u2013614. https:\/\/doi.org\/10.1109\/TPAMI.2011.158","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10934_CR188","doi-asserted-by":"publisher","unstructured":"Qi J, Yu J, Tu T, Gao K, Xu Y, Guan X, Wang X, Xu B, Hou L, Li J, Tang J (2023) Goal: A challenging knowledge-grounded video captioning benchmark for real-time soccer commentary generation. In: Proceedings of the 32nd ACM international conference on information and knowledge management. CIKM \u201923. Association for Computing Machinery, New York, NY, USA, pp 5391\u20135395. https:\/\/doi.org\/10.1145\/3583780.3615120","DOI":"10.1145\/3583780.3615120"},{"key":"10934_CR187","doi-asserted-by":"publisher","unstructured":"Qiu Q, Jiang Z, Chellappa R (2011) Sparse dictionary-based representation and recognition of action attributes. In: 2011 international conference on computer vision, pp 707\u2013714. https:\/\/doi.org\/10.1109\/ICCV.2011.6126307","DOI":"10.1109\/ICCV.2011.6126307"},{"key":"10934_CR189","doi-asserted-by":"crossref","unstructured":"Qu H, Cai Y, Liu J (2024) Llms are good action recognizers, 18395\u201318406","DOI":"10.1109\/CVPR52733.2024.01741"},{"key":"10934_CR190","doi-asserted-by":"crossref","unstructured":"Ramanathan V, Huang J, Abu-El-Haija S, Gorban AN, Murphy K, Fei-Fei L (2015) Detecting events and key actors in multi-person videos. CoRR abs\/1511.02917, 3043\u20133053. arXiv: 1511.02917","DOI":"10.1109\/CVPR.2016.332"},{"issue":"5","key":"10934_CR191","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/s00138-012-0450-4","volume":"24","author":"KK Reddy","year":"2013","unstructured":"Reddy KK, Shah M (2013) Recognizing 50 human action categories of web videos. Mach Vis Appl 24(5):971\u2013981. https:\/\/doi.org\/10.1007\/s00138-012-0450-4","journal-title":"Mach Vis Appl"},{"key":"10934_CR192","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement"},{"issue":"11","key":"10934_CR193","doi-asserted-by":"publisher","first-page":"16185","DOI":"10.1007\/s11042-019-08576-z","volume":"80","author":"Z Ren","year":"2021","unstructured":"Ren Z, Zhang Q, Gao X, Hao P, Cheng J (2021) Multi-modality learning for human action recognition. Multimed Tools Appl 80(11):16185\u201316203. https:\/\/doi.org\/10.1007\/s11042-019-08576-z","journal-title":"Multimed Tools Appl"},{"key":"10934_CR194","doi-asserted-by":"publisher","unstructured":"Rodriguez MD, Ahmed J, Shah M (2008) Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1\u20138. https:\/\/doi.org\/10.1109\/CVPR.2008.4587727","DOI":"10.1109\/CVPR.2008.4587727"},{"key":"10934_CR195","doi-asserted-by":"publisher","first-page":"2804","DOI":"10.1109\/ACCESS.2023.3234421","volume":"11","author":"L Romeo","year":"2023","unstructured":"Romeo L, Marani R, D\u2019Orazio T, Cicirelli G (2023) Video based mobility monitoring of elderly people using deep learning models. IEEE Access 11:2804\u20132819. https:\/\/doi.org\/10.1109\/ACCESS.2023.3234421","journal-title":"IEEE Access"},{"key":"10934_CR196","doi-asserted-by":"publisher","unstructured":"Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 international conference on computer vision, pp 2564\u20132571. https:\/\/doi.org\/10.1109\/ICCV.2011.6126544","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"10934_CR197","doi-asserted-by":"publisher","unstructured":"Sadanand S, Corso JJ (2012) Action bank: a high-level representation of activity in video. In: 2012 IEEE conference on computer vision and pattern recognition, pp 1234\u20131241. https:\/\/doi.org\/10.1109\/CVPR.2012.6247806","DOI":"10.1109\/CVPR.2012.6247806"},{"key":"10934_CR198","doi-asserted-by":"publisher","unstructured":"Safdarnejad SM, Liu X, Udpa L, Andrus B, Wood J, Craven D (2015) Sports Videos in the Wild (SVW): a video dataset for sports analysis. In: 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), vol 1, pp 1\u20137. https:\/\/doi.org\/10.1109\/FG.2015.7163105","DOI":"10.1109\/FG.2015.7163105"},{"key":"10934_CR199","doi-asserted-by":"publisher","unstructured":"Saha A, Sharma T, Batra H, Jain A, Pal V (2020) Human action recognition using smartphone sensors. In: 2020 international conference on computational performance evaluation (ComPE), pp 238\u2013243. https:\/\/doi.org\/10.1109\/ComPE49325.2020.9200169","DOI":"10.1109\/ComPE49325.2020.9200169"},{"issue":"5","key":"10934_CR200","doi-asserted-by":"publisher","first-page":"4145","DOI":"10.1007\/s00521-022-07937-4","volume":"35","author":"G Saleem","year":"2023","unstructured":"Saleem G, Bajwa UI, Raza RH (2023) Toward human activity recognition: a survey. Neural Comput Appl 35(5):4145\u20134182. https:\/\/doi.org\/10.1007\/s00521-022-07937-4","journal-title":"Neural Comput Appl"},{"key":"10934_CR201","doi-asserted-by":"publisher","unstructured":"Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 3, pp 32\u2013363. https:\/\/doi.org\/10.1109\/ICPR.2004.1334462","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"10934_CR202","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1016\/j.matpr.2021.12.004","volume":"57","author":"N Senthilkumar","year":"2022","unstructured":"Senthilkumar N, Manimegalai M, Karpakam S, Ashokkumar SR, Premkumar M (2022) Human action recognition based on spatial-temporal relational model and LSTM-CNN framework. Mater Today 57:2087\u20132091. https:\/\/doi.org\/10.1016\/j.matpr.2021.12.004","journal-title":"Mater Today"},{"issue":"12","key":"10934_CR203","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1167\/9.12.15","volume":"9","author":"HJ Seo","year":"2009","unstructured":"Seo HJ, Milanfar P (2009) Static and space-time visual saliency detection by self-resemblance. J Vis 9(12):15. https:\/\/doi.org\/10.1167\/9.12.15","journal-title":"J Vis"},{"key":"10934_CR204","doi-asserted-by":"crossref","unstructured":"Seweryn K, Wr\u00f3blewska A, Lukasik S (2023) Survey of action recognition, spotting and spatio-temporal localization in soccer\u2014current trends and research perspectives","DOI":"10.2139\/ssrn.4736989"},{"key":"10934_CR205","doi-asserted-by":"publisher","unstructured":"Shahroudy A, Liu J, Ng T-T, Wang G (2016) NTU RGB+D: a large scale dataset for 3D human activity analysis. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1010\u20131019. https:\/\/doi.org\/10.1109\/CVPR.2016.115","DOI":"10.1109\/CVPR.2016.115"},{"key":"10934_CR206","unstructured":"Shao D, Zhao Y, Dai B, Lin D (2020) FineGym: a hierarchical video dataset for fine-grained action understanding. CoRR abs\/2004.06704, 2616\u20132625. arXiv: 2004.06704"},{"key":"10934_CR207","unstructured":"Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. CoRR abs\/1511.04119. arXiv: 1511.04119"},{"key":"10934_CR208","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103386","volume":"82","author":"X Shen","year":"2022","unstructured":"Shen X, Ding Y (2022) Human skeleton representation for 3D action recognition based on complex network coding and LSTM. J Vis Commun Image Represent 82:103386. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103386","journal-title":"J Vis Commun Image Represent"},{"key":"10934_CR209","doi-asserted-by":"publisher","unstructured":"Shi Y, Zeng W, Huang T, Wang Y (2015) Learning Deep Trajectory Descriptor for action recognition in videos using deep neural networks. In: 2015 IEEE international conference on multimedia and expo (ICME), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME.2015.7177461","DOI":"10.1109\/ICME.2015.7177461"},{"key":"10934_CR210","doi-asserted-by":"crossref","unstructured":"Sigurdsson GA, Varol G, Wang X, Farhadi A, Laptev I, Gupta A (2016) Hollywood in homes: crowdsourcing data collection for activity understanding. CoRR abs\/1604.01753, 510\u2013526. arXiv: 1604.01753","DOI":"10.1007\/978-3-319-46448-0_31"},{"key":"10934_CR211","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. Adv Neural Inf Process Syst 27"},{"key":"10934_CR213","doi-asserted-by":"publisher","unstructured":"Singh B, Marks TK, Jones M, Tuzel O, Shao M (2016) A multi-stream bi-directional recurrent neural network for fine-grained action detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1961\u20131970. https:\/\/doi.org\/10.1109\/CVPR.2016.216","DOI":"10.1109\/CVPR.2016.216"},{"key":"10934_CR212","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.procs.2020.06.030","volume":"173","author":"V Singh","year":"2020","unstructured":"Singh V, Singh S, Gupta P (2020) Real-time anomaly recognition through CCTV using neural networks. Procedia Comput Sci 173:254\u2013263. https:\/\/doi.org\/10.1016\/j.procs.2020.06.030","journal-title":"Procedia Comput Sci"},{"key":"10934_CR214","doi-asserted-by":"crossref","unstructured":"Soomro K, Zamir AR (2014) Action recognition in realistic sports videos. In: Computer vision in sports, pp 181\u2013208. https:\/\/api.semanticscholar.org\/CorpusID:40082495","DOI":"10.1007\/978-3-319-09396-3_9"},{"key":"10934_CR215","unstructured":"Soomro K, Zamir AR, Shah M (2012) UCF101: a dataset of 101 human actions classes from videos in the wild. CoRR abs\/1212.0402. arXiv: 1212.0402"},{"key":"10934_CR216","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. CoRR abs\/1801.04264, 6479\u20136488. arXiv: 1801.04264"},{"key":"10934_CR217","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tpami.2022.3183112","volume":"45","author":"Z Sun","year":"2022","unstructured":"Sun Z, Ke Q, Rahmani H, Bennamoun M, Wang G, Liu J (2022) Human action recognition from various data modalities: a review. IEEE Trans Pattern Anal Mach Intell 45:1\u201320. https:\/\/doi.org\/10.1109\/tpami.2022.3183112","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10934_CR218","first-page":"3200","volume":"45","author":"Z Sun","year":"2022","unstructured":"Sun Z, Ke Q, Rahmani H, Bennamoun M, Wang G, Liu J (2022) Human action recognition from various data modalities: a review. IEEE Trans Pattern Anal Mach Intell 45(3):3200\u20133225","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10934_CR219","unstructured":"Sun R, Zhang Y, Shah T, Sun J, Zhang S, Li W, Duan H, Wei B, Ranjan R (2024) From sora what we can see: a survey of text-to-video generation. arXiv preprint arXiv:2405.10674"},{"key":"10934_CR220","first-page":"95","volume":"847","author":"A Takebayashi","year":"2020","unstructured":"Takebayashi A, Iwahori Y, Fukui S, Little JJ, Meng L, Wang A, Kijsirikul B (2020) Fall detection of elderly persons by action recognition using data augmentation and state transition diagram. Appl Comput Inf Technol 847:95\u2013109","journal-title":"Appl Comput Inf Technol"},{"key":"10934_CR221","first-page":"10078","volume":"35","author":"Z Tong","year":"2022","unstructured":"Tong Z, Song Y, Wang J, Wang L (2022) Videomae: masked autoencoders are data-efficient learners for self-supervised video pre-training. Adv Neural Inf Process Syst 35:10078\u201310093","journal-title":"Adv Neural Inf Process Syst"},{"key":"10934_CR222","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F, et\u00a0al (2023) Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971"},{"key":"10934_CR223","unstructured":"Tran D, Bourdev LD, Fergus R, Torresani L, Paluri M (2014) C3D: generic features for video analysis. CoRR abs\/1412.0767. arXiv: 1412.0767"},{"key":"10934_CR224","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M (2017) A closer look at spatiotemporal convolutions for action recognition. CoRR abs\/1711.11248, 6450\u20136459. arXiv: 1711.11248"},{"key":"10934_CR225","doi-asserted-by":"crossref","unstructured":"Tsunoda T, Komori Y, Matsugu M, Harada T (2017) Football action recognition using hierarchical lstm. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 99\u2013107","DOI":"10.1109\/CVPRW.2017.25"},{"issue":"7","key":"10934_CR226","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1007\/s11831-022-09772-1","volume":"29","author":"B Tyagi","year":"2022","unstructured":"Tyagi B, Nigam S, Singh R (2022) A review of deep learning techniques for crowd behavior analysis. Arch Comput Methods Eng 29(7):5427\u20135455. https:\/\/doi.org\/10.1007\/s11831-022-09772-1","journal-title":"Arch Comput Methods Eng"},{"issue":"7","key":"10934_CR227","doi-asserted-by":"publisher","first-page":"130","DOI":"10.3390\/jimaging9070130","volume":"9","author":"H Ullah","year":"2023","unstructured":"Ullah H, Munir A (2023) Human activity recognition using cascaded dual attention CNN and bi-directional GRU framework. J Imaging 9(7):130","journal-title":"J Imaging"},{"issue":"10","key":"10934_CR228","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3561971","volume":"55","author":"FUM Ullah","year":"2023","unstructured":"Ullah FUM, Obaidat MS, Ullah A, Muhammad K, Hijji M, Baik SW (2023) A comprehensive review on vision-based violence detection in surveillance videos. ACM Comput Surv 55(10):1\u201344. https:\/\/doi.org\/10.1145\/3561971. (Place: New York, NY, USA Publisher: Association for Computing Machinery)","journal-title":"ACM Comput Surv"},{"issue":"4","key":"10934_CR229","doi-asserted-by":"publisher","first-page":"4302","DOI":"10.1109\/TPAMI.2022.3193611","volume":"45","author":"E Vahdani","year":"2023","unstructured":"Vahdani E, Tian Y (2023) Deep learning-based action detection in untrimmed videos: a survey. IEEE Trans Pattern Anal Mach Intell 45(4):4302\u20134320. https:\/\/doi.org\/10.1109\/TPAMI.2022.3193611","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10934_CR230","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. CoRR abs\/1706.03762. arXiv: 1706.03762"},{"key":"10934_CR236","doi-asserted-by":"publisher","unstructured":"Wang H, Schmid C (2013) Action recognition with improved trajectories. In: 2013 IEEE international conference on computer vision, pp 3551\u20133558. https:\/\/doi.org\/10.1109\/ICCV.2013.441","DOI":"10.1109\/ICCV.2013.441"},{"issue":"1","key":"10934_CR231","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang H, Kl\u00e4ser A, Schmid C, Liu C-L (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60\u201379. https:\/\/doi.org\/10.1007\/s11263-012-0594-8","journal-title":"Int J Comput Vis"},{"key":"10934_CR238","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Gool LV (2016) Temporal segment networks: towards good practices for deep action recognition. CoRR 9912, 20\u201336. arXiv: 1608.00859","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"10934_CR233","unstructured":"Wang X, Girshick RB, Gupta A, He K (2017) Non-local Neural Networks. CoRR abs\/1711.07971, 7794\u20137803. arXiv: 1711.07971"},{"issue":"12","key":"10934_CR232","doi-asserted-by":"publisher","first-page":"5784","DOI":"10.3390\/app12125784","volume":"12","author":"Z Wang","year":"2022","unstructured":"Wang Z, Lu H, Jin J, Hu K (2022a) Human action recognition based on improved two-stream convolution network. Appl Sci 12(12):5784. https:\/\/doi.org\/10.3390\/app12125784","journal-title":"Appl Sci"},{"key":"10934_CR235","unstructured":"Wang Y, Li K, Li Y, He Y, Huang B, Zhao Z, Zhang H, Xu J, Liu Y, Wang Z, Xing S, Chen G, Pan J, Yu J, Wang Y, Wang L, Qiao Y (2022b) InternVideo: general video foundation models via generative and discriminative learning"},{"key":"10934_CR234","doi-asserted-by":"crossref","unstructured":"Wang L, Huang B, Zhao Z, Tong Z, He Y, Wang Y, Wang Y, Qiao Y (2023a) Videomae v2: scaling video masked autoencoders with dual masking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14549\u201314560","DOI":"10.1109\/CVPR52729.2023.01398"},{"key":"10934_CR237","doi-asserted-by":"crossref","unstructured":"Wang M, Xing J, Mei J, Liu Y, Jiang Y (2023b) Actionclip: adapting language-image pretrained models for video action recognition. IEEE Trans Neural Netw Learn Syst 1\u201313","DOI":"10.1109\/TNNLS.2023.3331841"},{"key":"10934_CR239","doi-asserted-by":"crossref","unstructured":"Washington P, Kline A, Mutlu OC, Leblanc, \u00c9, Hou C, Stockham NT, Paskov KM, Chrisman BS, Wall DP (2021) Activity recognition with moving cameras and few training examples: applications for detection of autism-related headbanging. CoRR abs\/2101.03478, 1\u20137 2101.03478","DOI":"10.1145\/3411763.3451701"},{"issue":"9","key":"10934_CR240","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1109\/TMM.2019.2953814","volume":"22","author":"H Wu","year":"2020","unstructured":"Wu H, Ma X, Li Y (2020) Convolutional networks with channel and STIPs attention model for action recognition in videos. IEEE Trans Multimed 22(9):2293\u20132306. https:\/\/doi.org\/10.1109\/TMM.2019.2953814","journal-title":"IEEE Trans Multimed"},{"key":"10934_CR242","doi-asserted-by":"crossref","unstructured":"Wu F, Wang Q, Bian J, Ding N, Lu F, Cheng J, Dou D, Xiong H (2022) A survey on video action recognition in sports: datasets, methods and applications. IEEE","DOI":"10.1109\/TMM.2022.3232034"},{"key":"10934_CR241","doi-asserted-by":"crossref","unstructured":"Wu T, He R, Wu G, Wang L (2024) Sportshhi: a dataset for human-human interaction detection in sports videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 18537\u201318546","DOI":"10.1109\/CVPR52733.2024.01754"},{"key":"10934_CR243","doi-asserted-by":"publisher","unstructured":"Xarles A, Escalera S, Moeslund TB, Clap\u00e9s A (2023) ASTRA: an action spotting TRAnsformer for soccer videos. In: Proceedings of the 6th international workshop on multimedia content analysis in sports. MMSports \u201923. Association for Computing Machinery, New York, NY, USA, pp 93\u2013102 (2023) event-place: Ottawa ON, Canada. https:\/\/doi.org\/10.1145\/3606038.3616153","DOI":"10.1145\/3606038.3616153"},{"key":"10934_CR244","doi-asserted-by":"crossref","unstructured":"Xiang W, Li C, Wang B, Wei X, Hua X-S, Zhang L (2022) Spatiotemporal self-attention modeling with temporal patch shift for action recognition","DOI":"10.1007\/978-3-031-20062-5_36"},{"issue":"1","key":"10934_CR245","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/info14010013","volume":"14","author":"J Xiao","year":"2022","unstructured":"Xiao J, Tian W, Ding L (2022a) Basketball action recognition method of deep neural network based on dynamic residual attention mechanism. Information 14(1):13. https:\/\/doi.org\/10.3390\/info14010013","journal-title":"Information"},{"key":"10934_CR246","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.neucom.2022.09.099","volume":"512","author":"S Xiao","year":"2022","unstructured":"Xiao S, Wang S, Huang Z, Wang Y, Jiang H (2022b) Two-stream transformer network for sensor-based human activity recognition. Neurocomputing 512:253\u2013268. https:\/\/doi.org\/10.1016\/j.neucom.2022.09.099","journal-title":"Neurocomputing"},{"key":"10934_CR247","doi-asserted-by":"publisher","unstructured":"Xu X, Hospedales T, Gong S (2015) Semantic embedding space for zero-shot action recognition. In: 2015 IEEE international conference on image processing (ICIP), pp 63\u201367. https:\/\/doi.org\/10.1109\/ICIP.2015.7350760","DOI":"10.1109\/ICIP.2015.7350760"},{"key":"10934_CR250","doi-asserted-by":"publisher","unstructured":"Yan C, Li X, Li G (2021) A new action recognition framework for video highlights summarization in sporting events. In: 2021 16th international conference on computer science & education (ICCSE), pp 653\u2013666. https:\/\/doi.org\/10.1109\/ICCSE51940.2021.9569708","DOI":"10.1109\/ICCSE51940.2021.9569708"},{"key":"10934_CR249","unstructured":"Yang Z, Luo T, Wang D, Hu Z, Gao J, Wang L (2018) Learning to navigate for fine-grained classification. CoRR abs\/1809.00287, 420\u2013435 . arXiv: 1809.00287"},{"key":"10934_CR248","doi-asserted-by":"crossref","unstructured":"Yang M, Gao H, Guo P, Wang L (2024) Adapting short-term transformers for action detection in untrimmed videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 18570\u201318579","DOI":"10.1109\/CVPR52733.2024.01757"},{"key":"10934_CR251","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","volume":"118","author":"G Yao","year":"2019","unstructured":"Yao G, Lei T, Zhong J (2019) A review of Convolutional-Neural-Network-based action recognition. Cooperative Soc Robots 118:14\u201322. https:\/\/doi.org\/10.1016\/j.patrec.2018.05.018","journal-title":"Cooperative Soc Robots"},{"key":"10934_CR252","doi-asserted-by":"publisher","unstructured":"Yenduri S, Chalavadi V, Mohan CK (2022) STIP-GCN: space-time interest points graph convolutional network for action recognition. In: 2022 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN55064.2022.9891959","DOI":"10.1109\/IJCNN55064.2022.9891959"},{"key":"10934_CR253","doi-asserted-by":"crossref","unstructured":"Yeung S, Russakovsky O, Jin N, Andriluka M, Mori G, Fei-Fei L (2015) Every moment counts: dense detailed labeling of actions in complex videos. CoRR 126, 375\u2013389. arXiv: 1507.05738","DOI":"10.1007\/s11263-017-1013-y"},{"key":"10934_CR254","doi-asserted-by":"publisher","unstructured":"Yuan F, Sahbi H, Prinet V (2011) Spatio-temporal context kernel for activity recognition. In: The first asian conference on pattern recognition, pp 436\u2013440. https:\/\/doi.org\/10.1109\/ACPR.2011.6166583","DOI":"10.1109\/ACPR.2011.6166583"},{"key":"10934_CR255","unstructured":"Yun S, Oh SJ, Heo B, Han D, Kim J (2020) VideoMix: rethinking data augmentation for video classification. CoRR abs\/2012.03457. arXiv: 2012.03457"},{"key":"10934_CR256","doi-asserted-by":"publisher","unstructured":"Zachariah L, Maharajan K (2023) Review on vision based human motion detection using deep learning. In: 2023 4th international conference on smart electronics and communication (ICOSEC). IEEE, Trichy, India, pp 1279\u20131287. https:\/\/doi.org\/10.1109\/ICOSEC58147.2023.10276103. Accessed 20 Dec 2023","DOI":"10.1109\/ICOSEC58147.2023.10276103"},{"key":"10934_CR257","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103870","volume":"94","author":"C Zalluhoglu","year":"2020","unstructured":"Zalluhoglu C, Ikizler-Cinbis N (2020) Collective sports: a multi-task dataset for collective activity recognition. Image Vis Comput 94:103870. https:\/\/doi.org\/10.1016\/j.imavis.2020.103870","journal-title":"Image Vis Comput"},{"issue":"2","key":"10934_CR258","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1007\/s13369-022-07236-z","volume":"48","author":"H Zan","year":"2023","unstructured":"Zan H, Zhao G (2023) Human action recognition research based on fusion TS-CNN and LSTM networks. Arab J Sci Eng 48(2):2331\u20132345. https:\/\/doi.org\/10.1007\/s13369-022-07236-z","journal-title":"Arab J Sci Eng"},{"issue":"5","key":"10934_CR259","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/s19051005","volume":"19","author":"H-B Zhang","year":"2019","unstructured":"Zhang H-B, Zhang Y-X, Zhong B, Lei Q, Yang L, Du J-X, Chen D-S (2019) A comprehensive survey of vision-based human action recognition methods. Sensors 19(5):1005. https:\/\/doi.org\/10.3390\/s19051005","journal-title":"Sensors"},{"key":"10934_CR261","doi-asserted-by":"publisher","unstructured":"Zhang Y, Li B, Fang H, Meng Q (2023) A multi-modal transformer approach for football event classification. In: 2023 IEEE international conference on image processing (ICIP), pp 2220\u20132224. https:\/\/doi.org\/10.1109\/ICIP49359.2023.10223172","DOI":"10.1109\/ICIP49359.2023.10223172"},{"key":"10934_CR260","unstructured":"Zhang Y, Bai Y, Wang H, Wang Y, Fu Y (2024) Don\u2019t judge by the look: a motion coherent augmentation for video recognition. arXiv preprint arXiv:2403.09506"},{"key":"10934_CR262","doi-asserted-by":"crossref","unstructured":"Zhao H, Torralba A, Torresani L, Yan Z (2019) HACS: human action clips and segments dataset for recognition and temporal localization","DOI":"10.1109\/ICCV.2019.00876"},{"key":"10934_CR263","unstructured":"Zhou X, Kang L, Cheng Z, He B, Xin J (2021) Feature combination meets attention: baidu soccer embeddings and transformer based temporal detection. arXiv. arXiv:2106.14447 [cs]. http:\/\/arxiv.org\/abs\/2106.14447. Accessed 29 Nov 2023"},{"key":"10934_CR269","doi-asserted-by":"publisher","unstructured":"Zhu G, Xu C, Huang Q, Gao W, Xing L (2006) Player action recognition in broadcast tennis video with applications to semantic analysis of sports game. In: Proceedings of the 14th ACM international conference on multimedia. MM \u201906. Association for Computing Machinery, New York, NY, USA. event-place: Santa Barbara, CA, USA, pp 431\u2013440. https:\/\/doi.org\/10.1145\/1180639.1180728","DOI":"10.1145\/1180639.1180728"},{"key":"10934_CR264","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1007\/978-3-642-19309-5_51","volume-title":"Computer vision\u2014ACCV 2010","author":"Y Zhu","year":"2011","unstructured":"Zhu Y, Zhao X, Fu Y, Liu Y (2011) Sparse coding on local spatial-temporal volumes for human action recognition. In: Kimmel R, Klette R, Sugimoto A (eds) Computer vision\u2014ACCV 2010, vol 6493. Springer, Berlin, pp 660\u2013671"},{"key":"10934_CR266","doi-asserted-by":"crossref","unstructured":"Zhu W, Lan C, Xing J, Zeng W, Li Y, Shen L, Xie X (2016) Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks","DOI":"10.1609\/aaai.v30i1.10451"},{"key":"10934_CR268","unstructured":"Zhu Y, Li X, Liu C, Zolfaghari M, Xiong Y, Wu C, Zhang Z, Tighe J, Manmatha R, Li M (2020a) A comprehensive study of deep video action recognition. CoRR abs\/2012.06567. arXiv: 2012.06567"},{"key":"10934_CR270","unstructured":"Zhu S, Yang T, Mendieta M, Chen C (2020b) A3D: adaptive 3D networks for video action recognition. CoRR abs\/2011.12384[SPACE]arXiv: 2011.12384"},{"key":"10934_CR267","doi-asserted-by":"publisher","unstructured":"Zhu H, Liang J, Lin C, Zhang J, Hu J (2022) A transformer-based system for action spotting in soccer videos. In: Proceedings of the 5th international ACM workshop on multimedia content analysis in sports. ACM, Lisboa Portugal, pp. 103\u2013109. https:\/\/doi.org\/10.1145\/3552437.3555693. Accessed 28 Nov 2023","DOI":"10.1145\/3552437.3555693"},{"issue":"22","key":"10934_CR265","doi-asserted-by":"publisher","first-page":"5001","DOI":"10.3390\/s19225001","volume":"19","author":"Z Zhuang","year":"2019","unstructured":"Zhuang Z, Xue Y (2019) Sport-related human activity detection and recognition using a smartwatch. Sensors 19(22):5001. https:\/\/doi.org\/10.3390\/s19225001","journal-title":"Sensors"},{"issue":"22","key":"10934_CR271","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.3390\/electronics11223797","volume":"11","author":"K Zuo","year":"2022","unstructured":"Zuo K, Su X (2022) Three-dimensional action recognition for basketball teaching coupled with deep neural network. Electronics 11(22):3797. https:\/\/doi.org\/10.3390\/electronics11223797","journal-title":"Electronics"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10934-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10934-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10934-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T22:06:42Z","timestamp":1729721202000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10934-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,16]]},"references-count":271,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["10934"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10934-9","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,16]]},"assertion":[{"value":"16 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"293"}}