{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:25:07Z","timestamp":1768879507080,"version":"3.49.0"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771319"],"award-info":[{"award-number":["61771319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076165"],"award-info":[{"award-number":["62076165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11042-022-13867-z","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T08:03:48Z","timestamp":1665129828000},"page":"15515-15533","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Dual attention based spatial-temporal inference network for volleyball group activity recognition"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8814-4628","authenticated-orcid":false,"given":"Yanshan","family":"Li","sequence":"first","affiliation":[]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hailin","family":"Zong","sequence":"additional","affiliation":[]},{"given":"Weixin","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"13867_CR1","doi-asserted-by":"crossref","unstructured":"Amer MR, Lei P, Todorovic S (2014) Hirf: hierarchical random field for collective activity recognition in videos. In: European conference on computer vision, Springer, Cham, pp 572\u2013585","DOI":"10.1007\/978-3-319-10599-4_37"},{"key":"13867_CR2","doi-asserted-by":"crossref","unstructured":"Amer MR, Todorovic S, Fern A et al (2013) Monte carlo tree search for scheduling activity recognition. In: IEEE international conference on computer vision, pp 1353\u20131360","DOI":"10.1109\/ICCV.2013.171"},{"issue":"4","key":"13867_CR3","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1109\/TPAMI.2015.2465955","volume":"38","author":"MR Amer","year":"2015","unstructured":"Amer MR, Todorovic S (2015) Sum product networks for activity recognition. IEEE Trans Pattern Anal Mach Intell 38(4):800\u2013813","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13867_CR4","doi-asserted-by":"crossref","unstructured":"Bagautdinov T, Alahi A, Fleuret F et al (2017) Social scene understanding: end-to-end multi-person action localization and collective activity recognition. In: IEEE conference on computer vision and pattern recognition, pp 4315\u20134324","DOI":"10.1109\/CVPR.2017.365"},{"key":"13867_CR5","doi-asserted-by":"crossref","unstructured":"Bastanfard A, Jafari S, Amirkhani D (2019) Improving tracking soccer players in shaded playfield video. In: 2019 5th Iranian conference on signal processing and intelligent systems (ICSPIS), IEEE, pp 1\u20138","DOI":"10.1109\/ICSPIS48872.2019.9066103"},{"key":"13867_CR6","doi-asserted-by":"crossref","unstructured":"Biswas S, Gall J (2018) Structural recurrent neural network (SRNN) for group activity analysis. In: IEEE winter conference on applications of computer vision, pp 1625\u20131632","DOI":"10.1109\/WACV.2018.00180"},{"key":"13867_CR7","doi-asserted-by":"crossref","unstructured":"Berlin SJ, John M (2020) Spiking neural network based on joint entropy of optical flow features for human action recognition. Vis Comput, 1\u201315","DOI":"10.1007\/s00371-020-02012-2"},{"issue":"9","key":"13867_CR8","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1080\/08839514.2020.1765110","volume":"34","author":"SJ Berlin","year":"2020","unstructured":"Berlin SJ, John M (2020) R-stdp based spiking neural network for human action recognition. Appl Artif Intell 34(9):656\u2013673","journal-title":"Appl Artif Intell"},{"key":"13867_CR9","doi-asserted-by":"crossref","unstructured":"Chen HY, Lai SH (2019) Group activity recognition via computing human pose motion history and collective map from video. In: Asian Conference on Pattern Recognition, Springer, Cham, pp 705\u2013 718","DOI":"10.1007\/978-3-030-41299-9_55"},{"key":"13867_CR10","doi-asserted-by":"crossref","unstructured":"Chen S, Tan X, Wang B et al (2018) Reverse attention for salient object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 234\u2013250","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"13867_CR11","unstructured":"Choi W, Shahid K, Savarese S (2009) What are they doing?: collective activity classification using spatio-temporal relationship among people. In: IEEE conference on computer vision workshops, pp 1282\u20131289"},{"issue":"18","key":"13867_CR12","doi-asserted-by":"publisher","first-page":"5162","DOI":"10.3390\/s20185162","volume":"20","author":"CL Chowdhary","year":"2020","unstructured":"Chowdhary CL, Patel PV, Kathrotia KJ, et al. (2020) Analytical study of hybrid techniques for image encryption and decryption. Sensors 20 (18):5162","journal-title":"Sensors"},{"key":"13867_CR13","doi-asserted-by":"crossref","unstructured":"Dasgupta A, Jawahar CV, Alahari K (2021) Context aware group activity recognition. In: 2020 25th international conference on pattern recognition (ICPR), IEEE, pp 10098\u201310105","DOI":"10.1109\/ICPR48806.2021.9412306"},{"key":"13867_CR14","doi-asserted-by":"crossref","unstructured":"Deng Z, Zhai M, Chen L et al (2015) Deep structured models for group activity recognition, arXiv:1506.04191","DOI":"10.5244\/C.29.179"},{"key":"13867_CR15","doi-asserted-by":"crossref","unstructured":"Fan DP, Wang W, Cheng MM et al (2019) Shifting more attention to video salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8554\u20138564","DOI":"10.1109\/CVPR.2019.00875"},{"key":"13867_CR16","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: IEEE conference on computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"13867_CR17","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H et al (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"13867_CR18","doi-asserted-by":"crossref","unstructured":"Hajimirsadeghi H, Yan W, Vahdat A, et al. (2015) Visual recognition by counting instances: a multi-instance cardinality potential kernel. In: IEEE conference on computer vision and pattern recognition, pp 2596\u20132605","DOI":"10.1109\/CVPR.2015.7298875"},{"key":"13867_CR19","doi-asserted-by":"crossref","unstructured":"Han M, Zhang DJ, Wang Y et al (2022) Dual-AI: dual-path actor interaction learning for group activity recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2990\u20132999","DOI":"10.1109\/CVPR52688.2022.00300"},{"key":"13867_CR20","doi-asserted-by":"crossref","unstructured":"Hajimirsadeghi H, Yan W, Vahdat A et al (2015) Visual recognition by counting instances: a multi-instance cardinality potential kernel. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2596\u20132605","DOI":"10.1109\/CVPR.2015.7298875"},{"issue":"23","key":"13867_CR21","doi-asserted-by":"publisher","first-page":"4941","DOI":"10.3390\/rs13234941","volume":"13","author":"R Hussain","year":"2021","unstructured":"Hussain R, Karbhari Y, Ijaz MF et al (2021) Revise-net: exploiting reverse attention mechanism for salient object detection. Remote Sens 13(23):4941","journal-title":"Remote Sens"},{"key":"13867_CR22","doi-asserted-by":"crossref","unstructured":"Hu X, Yang K, Fei L et al (2019) Acnet: attention based network to exploit complementary features for rgbd semantic segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), IEEE, pp 1440\u20131444","DOI":"10.1109\/ICIP.2019.8803025"},{"key":"13867_CR23","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"13867_CR24","doi-asserted-by":"crossref","unstructured":"Ibrahim MS, Muralidharan S, Deng Z, Vahdat A, Mori G (2016) A hierarchical deep temporal model for group activity recognition. IEEE Comput Soc Conf Comput Vis Pattern Recognit","DOI":"10.1109\/CVPR.2016.217"},{"key":"13867_CR25","doi-asserted-by":"crossref","unstructured":"Ibrahim MS, Muralidharan S, Deng Z, Vahdat A, Mori G (2016) Hierarchical deep temporal models for group activity recognition. arXiv:1607.02643","DOI":"10.1109\/CVPR.2016.217"},{"key":"13867_CR26","doi-asserted-by":"crossref","unstructured":"Ibrahim MS, Mori G (2018) Hierarchical relational networks for group activity recognition and retrieval. In: Proceedings of the European conference on computer vision (ECCV), pp 721\u2013736","DOI":"10.1007\/978-3-030-01219-9_44"},{"key":"13867_CR27","doi-asserted-by":"crossref","unstructured":"Islam MM, Iqbal T (2020) Hamlet: a hierarchical multimodal attention-based human activity recognition algorithm. In: 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 10285\u201310292","DOI":"10.1109\/IROS45743.2020.9340987"},{"key":"13867_CR28","unstructured":"Jianchao W, Limin W, Li W, Jie G, Gangshan W (2019) Learning actor relation graphs for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 9964\u20139974"},{"key":"13867_CR29","doi-asserted-by":"crossref","unstructured":"Lamghari S, Bilodeau GA, Saunier N (2021) A grid-based representation for human action recognition. In: 25th international conference on pattern recognition (ICPR), pp 10500\u201310507","DOI":"10.1109\/ICPR48806.2021.9413136"},{"key":"13867_CR30","unstructured":"Lan T, Sigal L, Mori G (2012) Social roles in hierarchical models for human activity recognition. In: IEEE conference on computer vision and pattern recognition, pp 1354\u20131361"},{"key":"13867_CR31","doi-asserted-by":"crossref","unstructured":"Li X, Choo Chuah M (2017) Sbgar: semantics based group activity recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2876\u20132885","DOI":"10.1109\/ICCV.2017.313"},{"key":"13867_CR32","doi-asserted-by":"crossref","unstructured":"Li X, Choo Chuah M (2017) Sbgar: semantics based group activity recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2876\u20132885","DOI":"10.1109\/ICCV.2017.313"},{"issue":"4","key":"13867_CR33","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TIP.2017.2785279","volume":"27","author":"J Liu","year":"2017","unstructured":"Liu J, Wang G, Duan LY et al (2017) Skeleton-based human action recognition with global context-aware attention LSTM networks. IEEE Trans Image Process 27(4):1586\u20131599","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"13867_CR34","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/TNNLS.2017.2669522","volume":"29","author":"H Liu","year":"2017","unstructured":"Liu H, Shu N, Tang Q, Zhang W (2017) Computational model based on neural network of visual cortex for human action recognition. IEEE Trans Neural Netw Learn Syst 29(5):1427\u201340","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"13867_CR35","doi-asserted-by":"crossref","unstructured":"Peng X, Schmid C (2016) Multi-region two-stream r-CNN for action detection. In: European conference on computer vision, Springer, Cham, pp 744\u2013759","DOI":"10.1007\/978-3-319-46493-0_45"},{"key":"13867_CR36","first-page":"122","volume":"108360","author":"M Perez","year":"2022","unstructured":"Perez M, Liu J, Kot AC (2022) Skeleton-based relational reasoning for group activity analysis. Pattern Recogn 108360:122","journal-title":"Pattern Recogn"},{"key":"13867_CR37","doi-asserted-by":"crossref","unstructured":"Qi M, Qin J, Li A et al (2018) stagnet: an attentive semantic rnn for group activity recognition. In: Proceedings of the european conference on computer vision (ECCV), pp 101\u2013117","DOI":"10.1007\/978-3-030-01249-6_7"},{"issue":"2","key":"13867_CR38","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/TCSVT.2019.2894161","volume":"30","author":"M Qi","year":"2020","unstructured":"Qi M, Wang Y, Qin J et al (2020) stagNet: an attentive semantic RNN for group activity and individual action recognition. IEEE Trans Circuits Syst Video Technol 30(2):549\u2013565","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"47","key":"13867_CR39","doi-asserted-by":"publisher","first-page":"35275","DOI":"10.1007\/s11042-019-7702-5","volume":"79","author":"A Ramchandran","year":"2020","unstructured":"Ramchandran A, Sangaiah AK (2020) Unsupervised deep learning system for local anomaly event detection in crowded scenes. Multimed Tools Appl 79 (47):35275\u201335295","journal-title":"Multimed Tools Appl"},{"key":"13867_CR40","doi-asserted-by":"crossref","unstructured":"Rao Y, Lu J, Zhou J (2017) Attention-aware deep reinforcement learning for video face recognition. In: Proceedings of the IEEE international conference on computer vision, pp 3931\u20133940","DOI":"10.1109\/ICCV.2017.424"},{"key":"13867_CR41","doi-asserted-by":"crossref","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 421\u2013429","DOI":"10.1007\/978-3-030-00928-1_48"},{"issue":"2","key":"13867_CR42","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s11263-010-0355-5","volume":"93","author":"MS Ryoo","year":"2011","unstructured":"Ryoo MS, Aggarwal JK (2011) Stochastic representation and recognition of high-level group activities: describing structural uncertainties in human activities. Int J Comput Vis 93(2):183\u2013200","journal-title":"Int J Comput Vis"},{"key":"13867_CR43","unstructured":"Salehifar H, Dehshibi MM, Bastanfard A (2011) A fast algorithm for detecting, labeling and tracking volleyball players in sport videos. In: IEEE ICSAP, pp 398\u2013401"},{"key":"13867_CR44","unstructured":"Salehifar H, Bastanfard A (2011) Visual tracking of athletes in volleyball sport videos. In: Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV), p 1"},{"key":"13867_CR45","unstructured":"Salehifar H, Bastanfard A (2011) A complete view depended volleyball video dataset under the uncontrolled conditions. In: Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV). The steering committee of the world congress in computer science, computer engineering and applied computing (WorldComp), p 1"},{"issue":"11","key":"13867_CR46","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681","journal-title":"IEEE Trans Signal Process"},{"key":"13867_CR47","unstructured":"Shu T, Xie D, Rothrock B et al (2015) Joint inference of groups, events and human roles in aerial videos. In: IEEE conference on computer vision and pattern recognition, pp 4576\u20134584"},{"key":"13867_CR48","doi-asserted-by":"crossref","unstructured":"Shu T, Todorovic S, Zhu SC (2017) CERN: confidence-energy recurrent network for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 4255\u20134263","DOI":"10.1109\/CVPR.2017.453"},{"key":"13867_CR49","doi-asserted-by":"crossref","unstructured":"Singh G, Saha S, Sapienza M et al (2017) Online real-time multiple spatiotemporal action localisation and prediction. In: IEEE international conference on computer vision, pp 3637\u20133646","DOI":"10.1109\/ICCV.2017.393"},{"key":"13867_CR50","doi-asserted-by":"crossref","unstructured":"Song S, Lan C, Xing J, Zeng W, Liu J (2017) An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: AAAI, pp 4263\u20134270","DOI":"10.1609\/aaai.v31i1.11212"},{"issue":"1","key":"13867_CR51","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s11042-012-1088-y","volume":"70","author":"A Talukder","year":"2014","unstructured":"Talukder A, Panangadan A (2014) Extreme event detection and assimilation from multimedia sources. Multimed Tools Appl 70(1):237\u2013261","journal-title":"Multimed Tools Appl"},{"key":"13867_CR52","doi-asserted-by":"publisher","first-page":"18762","DOI":"10.1109\/ACCESS.2021.3054250","volume":"9","author":"J Tamang","year":"2021","unstructured":"Tamang J, Nkapkop JDD, Ijaz MF, et al. (2021) Dynamical properties of ion-acoustic waves in space plasma and its application to image encryption. IEEE Access 9:18762\u201318782","journal-title":"IEEE Access"},{"key":"13867_CR53","doi-asserted-by":"crossref","unstructured":"Tang Y, Wang Z, Li P et al (2018) Mining semantics-preserving attention for group activity recognition. In: Proceedings of the 26th ACM international conference on multimedia, pp 1283\u20131291","DOI":"10.1145\/3240508.3240576"},{"key":"13867_CR54","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need, Advan Neural Inform Process Syst, 30"},{"key":"13867_CR55","doi-asserted-by":"crossref","unstructured":"Wang Z, Shi Q, Shen C, et al. (2013) Bilinear programming for human activity recognition with unknown MRF graphs. In: IEEE conference on computer vision and pattern recognition, pp 1690\u20131697","DOI":"10.1109\/CVPR.2013.221"},{"key":"13867_CR56","doi-asserted-by":"crossref","unstructured":"Wang M, Ni B, Yang X (2017) Recurrent modeling of interaction context for collective activity recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3048\u20133056","DOI":"10.1109\/CVPR.2017.783"},{"key":"13867_CR57","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A et al (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"issue":"8","key":"13867_CR58","first-page":"1465","volume":"48","author":"CX Wang","year":"2020","unstructured":"Wang CX, Xue H (2020) Group activity recognition based on GFU and hierarchical LSTM. Acta Electron Sin 48(8):1465\u20131471","journal-title":"Acta Electron Sin"},{"key":"13867_CR59","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY et al (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"13867_CR60","doi-asserted-by":"crossref","unstructured":"Xie S, Sun C, Huang J et al (2018) Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In: European conference on computer vision (ECCV), pp 305\u2013321","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"13867_CR61","doi-asserted-by":"publisher","first-page":"65689","DOI":"10.1109\/ACCESS.2020.2979742","volume":"8","author":"D Xu","year":"2020","unstructured":"Xu D, Fu H, Wu L et al (2020) Group activity recognition by using effective multiple modality relation representation with temporal-spatial attention. IEEE Access 8:65689\u201365698","journal-title":"IEEE Access"},{"key":"13867_CR62","doi-asserted-by":"crossref","unstructured":"Yang J, Ren P, Zhang D et al (2017) Neural aggregation network for video face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4362\u20134371","DOI":"10.1109\/CVPR.2017.554"},{"issue":"60","key":"13867_CR63","first-page":"2021","volume":"15","author":"S Yang","year":"1109","unstructured":"Yang S, Gao T, Wang J et al (1109) Efficient spike-driven learning with dendritic event-based processing. Front Neurosci 15(60):2021","journal-title":"Front Neurosci"},{"key":"13867_CR64","doi-asserted-by":"crossref","unstructured":"Yang S, Wang J, Deng B et al (2021) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2021.3084250"},{"issue":"1","key":"13867_CR65","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TNNLS.2019.2899936","volume":"31","author":"S Yang","year":"2019","unstructured":"Yang S, Deng B, Wang J et al (2019) Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans Neural Netw Learn Syst 31(1):148\u2013162","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"13867_CR66","doi-asserted-by":"crossref","unstructured":"Yang S, Wang J, Zhang N et al (2021) Cerebellumorphic: large-scale neuromorphic model and architecture for supervised motor learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2021.3057070"},{"key":"13867_CR67","doi-asserted-by":"crossref","unstructured":"Yan R, Tang J, Shu X et al (2018) Participation-contributed temporal dynamic model for group activity recognition. In: Proceedings of the 26th ACM international conference on multimedia, pp 1292\u20131300","DOI":"10.1145\/3240508.3240572"},{"issue":"4","key":"13867_CR68","first-page":"3261","volume":"35","author":"H Yuan","year":"2021","unstructured":"Yuan H, Ni D (2021) Learning visual context for group activity recognition. Proc AAAI Conf Artif Intell 35(4):3261\u20133269","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"13867_CR69","doi-asserted-by":"crossref","unstructured":"Zalluhoglu C, Ikizler-Cinbis N Region based multi-stream convolutional neural networks for collective activity recognition. J Visual Commun Image Represent 2019(60):170\u2013179","DOI":"10.1016\/j.jvcir.2019.02.016"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13867-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13867-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13867-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T11:39:52Z","timestamp":1679657992000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13867-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,7]]},"references-count":69,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["13867"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13867-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,7]]},"assertion":[{"value":"10 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2022","order":4,"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 that they have no confict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}