{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:06:30Z","timestamp":1774533990148,"version":"3.50.1"},"reference-count":135,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11042-023-14925-w","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T04:36:12Z","timestamp":1677731772000},"page":"32635-32709","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Data-driven enabled approaches for criteria-based video summarization: a comprehensive survey, taxonomy, and future directions"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-7171","authenticated-orcid":false,"given":"Ambreen","family":"Sabha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arvind","family":"Selwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"14925_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal JK, Ryoo MS (2011) Human activity analysis: A review. ACM Computing Surveys 43(3):1\u201343","DOI":"10.1145\/1922649.1922653"},{"key":"14925_CR2","doi-asserted-by":"crossref","unstructured":"Agyeman R, Muhammad R, Choi GS (2019) Soccer Video Summarization Using Deep Learning. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) 2019 Mar 28, pp. 270\u2013273","DOI":"10.1109\/MIPR.2019.00055"},{"key":"14925_CR3","doi-asserted-by":"crossref","unstructured":"Ahmad Z, Illanko K, Khan N, Androutsos D (2019) Human action recognition using convolutional neural network and depth sensor data. In: Proceedings of the 2019 International Conference on Information Technology and Computer Communications 2019 Aug 16, pp. 1\u20135","DOI":"10.1145\/3355402.3355419"},{"key":"14925_CR4","doi-asserted-by":"crossref","unstructured":"Ali H, Sharif M, Yasmin M, Rehmani MH, Riaz F (2020) A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract. Artif Intell Rev 53:2635\u20132707","DOI":"10.1007\/s10462-019-09743-2"},{"key":"14925_CR5","doi-asserted-by":"crossref","unstructured":"Ali JJ, Shati NM, Gaata MT (2020) Abnormal activity detection in surveillance video scenes. Telkomnika (Telecommun Comput Electron Control) 18(5):2447\u20132453","DOI":"10.12928\/telkomnika.v18i5.16634"},{"key":"14925_CR6","doi-asserted-by":"crossref","unstructured":"Benjak J, Hofman D, Knezovi\u0107 J, \u017dagar M (2022) Performance Comparison of H. 264 and H. 265 Encoders in a 4K FPV Drone Piloting System. Appl Sci 12(13):6386","DOI":"10.3390\/app12136386"},{"issue":"4","key":"14925_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2601097.2601198","volume":"33","author":"I Arev","year":"2014","unstructured":"Arev I, Park HS, Sheikh Y, Hodgins J, Shamir A (2014) Automatic editing of footage from multiple social cameras. ACM Trans Graph 33(4):1\u201311. https:\/\/doi.org\/10.1145\/2601097.2601198","journal-title":"ACM Trans. Graph."},{"issue":"12","key":"14925_CR8","doi-asserted-by":"publisher","first-page":"8585","DOI":"10.1007\/s00521-019-04365-9","volume":"32","author":"MF Aslan","year":"2020","unstructured":"Aslan MF, Durdu A, Sabanci K (2020) Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization. Neural Comput. & Applic. 32(12):8585\u20138597. https:\/\/doi.org\/10.1007\/s00521-019-04365-9","journal-title":"Neural Comput. & Applic."},{"key":"14925_CR9","unstructured":"B. World (2019) World Population Ageing 2019. [Online]. Available: http:\/\/link.springer.com\/chapter\/10.1007\/978-94-007-5204-7_6"},{"key":"14925_CR10","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/3-540-45113-7_30","volume":"2728","author":"M Baillie","year":"2003","unstructured":"Baillie M, Jose JM (2003) Audio-based event detection for sports video. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2728:300\u2013309. https:\/\/doi.org\/10.1007\/3-540-45113-7_30","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"14925_CR11","doi-asserted-by":"crossref","unstructured":"Basavarajaiah M, Sharma P (2019) Survey of Compressed Domain Video Summarization. ACM Comput Surv 52(6):1\u201329","DOI":"10.1145\/3355398"},{"key":"14925_CR12","unstructured":"Bir B (2020) Wildfires, forest fires around world in 2020. https:\/\/www.aa.com.tr\/en\/environment\/wildfires-forest-fires-around-world-in-2020\/2088198"},{"key":"14925_CR13","unstructured":"Bojukyan E (2022) 52 video marketing statistics 2022 [infographic]. https:\/\/www.renderforest.com\/blog\/video-marketing-statistics. Accessed 14 Jan 2022"},{"key":"14925_CR14","doi-asserted-by":"publisher","unstructured":"Calic J, Izquierdo E (2002) Efficient key-frame extraction and video analysis. Proceedings - International Conference on Information Technology: Coding and Computing, ITCC 2002, pp 28\u201333. https:\/\/doi.org\/10.1109\/ITCC.2002.1000355.","DOI":"10.1109\/ITCC.2002.1000355"},{"issue":"6","key":"14925_CR15","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.cviu.2013.01.013","volume":"117","author":"JM Chaquet","year":"2013","unstructured":"Chaquet JM, Carmona EJ, Fern\u00e1ndez-Caballero A (2013) A survey of video datasets for human action and activity recognition. Comput Vis Image Underst 117(6):633\u2013659. https:\/\/doi.org\/10.1016\/j.cviu.2013.01.013","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"14925_CR16","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.cag.2012.02.010","volume":"36","author":"T Chen","year":"2012","unstructured":"Chen T, Lu A, Hu SM (2012) Visual storylines: semantic visualization of movie sequence. Elsevier 36(4):241\u2013249. https:\/\/doi.org\/10.1016\/j.cag.2012.02.010","journal-title":"Elsevier"},{"key":"14925_CR17","doi-asserted-by":"publisher","unstructured":"Choro\u015b K (2014) Categorization of sports video shots and scenes in tv sports news based on ball detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8397 LNAI, no. PART 1, pp 591\u2013600. https:\/\/doi.org\/10.1007\/978-3-319-05476-6_60.","DOI":"10.1007\/978-3-319-05476-6_60"},{"issue":"3","key":"14925_CR18","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00371-015-1066-2","volume":"32","author":"D Das Dawn","year":"2016","unstructured":"Das Dawn D, Shaikh SH (2016) A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector. Vis Comput 32(3):289\u2013306. https:\/\/doi.org\/10.1007\/s00371-015-1066-2","journal-title":"Vis. Comput."},{"key":"14925_CR19","doi-asserted-by":"publisher","first-page":"29253","DOI":"10.1109\/ACCESS.2019.2902507","volume":"7","author":"A Dilawari","year":"2019","unstructured":"Dilawari A, Khan MUG (2019) ASoVS: abstractive summarization of video sequences. IEEE Access 7:29253\u201329263. https:\/\/doi.org\/10.1109\/ACCESS.2019.2902507","journal-title":"IEEE Access"},{"key":"14925_CR20","unstructured":"Donchev D (2022) \u201c40 Mind Blowing YouTube Facts, Figures and Statistics \u2013 2022,\u201d. https:\/\/fortunelords.com\/youtube-statistics\/#:~:text=300 hours of video are,on Youtube every single day.&text=In an average month%2C 8,to a pay-TV service."},{"issue":"4","key":"14925_CR21","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1109\/TASLP.2015.2405481","volume":"23","author":"D Dov","year":"2015","unstructured":"Dov D, Talmon R, Cohen I (2015) Audio-visual voice activity detection using diffusion maps. IEEE Trans Audio Speech Lang Process 23(4):732\u2013745. https:\/\/doi.org\/10.1109\/TASLP.2015.2405481","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"issue":"2","key":"14925_CR22","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/s10489-020-01823-z","volume":"51","author":"O Elharrouss","year":"2021","unstructured":"Elharrouss O, Almaadeed N, Al-Maadeed S, Bouridane A, Beghdadi A (2021) A combined multiple action recognition and summarization for surveillance video sequences. Appl Intell 51(2):690\u2013712. https:\/\/doi.org\/10.1007\/s10489-020-01823-z","journal-title":"Appl. Intell."},{"key":"14925_CR23","doi-asserted-by":"publisher","unstructured":"Evangelopoulos G et al. (2009) \u201cVideo event detection and summarization using audio, visual and text saliency,\u201d ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, no. April, pp. 3553\u20133556, https:\/\/doi.org\/10.1109\/ICASSP.2009.4960393.","DOI":"10.1109\/ICASSP.2009.4960393"},{"key":"14925_CR24","doi-asserted-by":"publisher","unstructured":"Fei M, Jiang W, Mao W (2018) \u201cCreating personalized video summaries via semantic event detection,\u201d J. Ambient. Intell. Humaniz. Comput., vol. 0, no. 0, pp. 1\u201312, https:\/\/doi.org\/10.1007\/s12652-018-0797-0.","DOI":"10.1007\/s12652-018-0797-0"},{"issue":"6","key":"14925_CR25","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1007\/s11760-014-0645-4","volume":"8","author":"W Feng","year":"2014","unstructured":"Feng W, Liu R, Zhu M (2014) Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera. SIViP 8(6):1129\u20131138. https:\/\/doi.org\/10.1007\/s11760-014-0645-4","journal-title":"SIViP"},{"key":"14925_CR26","unstructured":"Furini M, Ghini V (2006) \u201c<(34) an Audio-Video Summarization Scheme Based on Audio and Video Analysis.Pdf>,\u201d pp. 1209\u20131213"},{"issue":"1","key":"14925_CR27","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s11042-009-0307-7","volume":"46","author":"M Furini","year":"2010","unstructured":"Furini M, Geraci F, Montangero M, Pellegrini M (2010) STIMO: STIll and MOving video storyboard for the web scenario. Multimed. Tools Appl. 46(1):47\u201369. https:\/\/doi.org\/10.1007\/s11042-009-0307-7","journal-title":"Multimed. Tools Appl."},{"key":"14925_CR28","unstructured":"G. of India (2020) \u201cAccidental Deaths and Suicides in India by NCRB,\u201dhttps:\/\/ncrb.gov.in\/en\/accidental-deaths-suicides-in-india?page=1"},{"key":"14925_CR29","doi-asserted-by":"crossref","unstructured":"Ghafoor HA, Javed A, Irtaza A, Dawood H, Dawood H, Banjar A (2018) Egocentric Video Summarization Based on People Interaction Using Deep Learning. vol. 2018","DOI":"10.1155\/2018\/7586417"},{"key":"14925_CR30","doi-asserted-by":"crossref","unstructured":"Ghatak S, Rup S, Majhi B, Swamy MNS (2020) An improved surveillance video synopsis framework: a HSATLBO optimization approach. Multimed Tools Appl 79(7\u20138):4429\u20134461","DOI":"10.1007\/s11042-019-7389-7"},{"key":"14925_CR31","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/cvpr.2000.854772","volume":"2","author":"Y Gong","year":"2000","unstructured":"Gong Y, Liu X (2000) Video summarization using singular value decomposition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2:174\u2013180. https:\/\/doi.org\/10.1109\/cvpr.2000.854772","journal-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"},{"key":"14925_CR32","doi-asserted-by":"publisher","unstructured":"Gong F et al. (2019) A real-time fire detection method from video with multifeature fusion. Comput Intell Neurosci vol 2019. https:\/\/doi.org\/10.1155\/2019\/1939171.","DOI":"10.1155\/2019\/1939171"},{"key":"14925_CR33","doi-asserted-by":"publisher","unstructured":"Guan G, Wang Z, Mei S, Ott M, He M, Feng DD (2014) A top-down approach for video summarization. ACM Trans Multimed Comput Commun Appl 11(1). https:\/\/doi.org\/10.1145\/2632267.","DOI":"10.1145\/2632267"},{"issue":"10","key":"14925_CR34","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.1016\/j.patcog.2014.04.018","volume":"47","author":"G Guo","year":"2014","unstructured":"Guo G, Lai A (2014) A survey on still image based human action recognition. Pattern Recogn 47(10):3343\u20133361. https:\/\/doi.org\/10.1016\/j.patcog.2014.04.018","journal-title":"Pattern Recogn."},{"key":"14925_CR35","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.patrec.2017.08.015","volume":"107","author":"Y Han","year":"2018","unstructured":"Han Y, Zhang P, Zhuo T, Huang W, Zhang Y (2018) Going deeper with two-stream ConvNets for action recognition in video surveillance. Pattern Recogn Lett 107:83\u201390. https:\/\/doi.org\/10.1016\/j.patrec.2017.08.015","journal-title":"Pattern Recogn. Lett."},{"key":"14925_CR36","doi-asserted-by":"publisher","unstructured":"He L, Wen S, Wang L, Li F (2020) Vehicle theft recognition from surveillance video based on spatiotemporal attention. Appl Intell pp 2128\u20132143. https:\/\/doi.org\/10.1007\/s10489-020-01933-8.","DOI":"10.1007\/s10489-020-01933-8"},{"key":"14925_CR37","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/CVPR.2015.7298698","volume":"07-12-June","author":"FC Heilbron","year":"2015","unstructured":"Heilbron FC, Escorcia V, Ghanem B, Niebles JC (2015) ActivityNet: A large-scale video benchmark for human activity understanding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 07-12-June:961\u2013970. https:\/\/doi.org\/10.1109\/CVPR.2015.7298698","journal-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"},{"issue":"9","key":"14925_CR38","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1109\/TCSVT.2010.2057020","volume":"20","author":"L Herranz","year":"2010","unstructured":"Herranz L, Martinez JM (2010) A framework for scalable summarization of video. IEEE Trans Circ Syst Vid Technol 20(9):1265\u20131270. https:\/\/doi.org\/10.1109\/TCSVT.2010.2057020","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"issue":"2","key":"14925_CR39","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TCSVT.2019.2890899","volume":"30","author":"C Huang","year":"2020","unstructured":"Huang C, Wang H (2020) A novel key-frames selection framework for comprehensive video summarization. IEEE Trans Circ Syst Vid Technol 30(2):577\u2013589. https:\/\/doi.org\/10.1109\/TCSVT.2019.2890899","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"key":"14925_CR40","doi-asserted-by":"publisher","unstructured":"Hussain T et al. (2021) A comprehensive survey of multi-view video summarization. Elsevier 109. https:\/\/doi.org\/10.1016\/j.patcog.2020.107567.","DOI":"10.1016\/j.patcog.2020.107567"},{"key":"14925_CR41","doi-asserted-by":"publisher","unstructured":"Hussein F, Piccardi M (2017) V-Jaune. ACM Trans. Multimed. Comput. Commun. Appl 13(2):1\u201319. https:\/\/doi.org\/10.1145\/3063532","DOI":"10.1145\/3063532"},{"key":"14925_CR42","doi-asserted-by":"crossref","unstructured":"Iosifidis A, Mouroutsos SG, Gasteratos A (2010) Real-time video surveillance by a hybrid static\/active camera mechatronic system. Int Conf Adv Intell Mechatron pp 84\u201389","DOI":"10.1109\/AIM.2010.5695742"},{"key":"14925_CR43","doi-asserted-by":"publisher","unstructured":"Itazuri T, Fukusato T, Yamaguchi S, Morishima S (2017) Court-Based Volleyball Video Summarization Focusing on Rally Scene. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017-July, pp. 179\u2013186, https:\/\/doi.org\/10.1109\/CVPRW.2017.28.","DOI":"10.1109\/CVPRW.2017.28"},{"key":"14925_CR44","doi-asserted-by":"publisher","unstructured":"Jegham I, Khalifa AB, Alouani I, Mahjoub MA (2019) MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset, vol. 11678 LNCS. Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-29888-3_42.","DOI":"10.1007\/978-3-030-29888-3_42"},{"key":"14925_CR45","doi-asserted-by":"publisher","first-page":"200901","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 Digit Investig 32:200901. https:\/\/doi.org\/10.1016\/j.fsidi.2019.200901","journal-title":"Forensic Sci Int Digit Investig"},{"key":"14925_CR46","doi-asserted-by":"publisher","unstructured":"Jeyanthi Suresh A, Visumathi J (2020) Inception ResNet deep transfer learning model for human action recognition using LSTM. Materials Today: Proceedings, no. xxxx. https:\/\/doi.org\/10.1016\/j.matpr.2020.09.609.","DOI":"10.1016\/j.matpr.2020.09.609"},{"issue":"6","key":"14925_CR47","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1109\/TCSVT.2019.2904996","volume":"30","author":"Z Ji","year":"2020","unstructured":"Ji Z, Xiong K, Pang Y, Li X (2020) Video summarization with attention-based encoder-decoder networks. IEEE Trans Circ Syst Vid Technol 30(6):1709\u20131717. https:\/\/doi.org\/10.1109\/TCSVT.2019.2904996","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"key":"14925_CR48","doi-asserted-by":"publisher","unstructured":"Kakadiya R, Lemos R, Mangalan S, Pillai M, Nikam S (2019) \u201cAI Based Automatic Robbery\/Theft Detection using Smart Surveillance in Banks,\u201d Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, pp. 201\u2013204, https:\/\/doi.org\/10.1109\/ICECA.2019.8822186.","DOI":"10.1109\/ICECA.2019.8822186"},{"key":"14925_CR49","doi-asserted-by":"publisher","unstructured":"Kalaivani P, Roomi SMM (2017) Towards comprehensive understanding of event detection and video summarization approaches. Proceedings - 2017 2nd International Conference on Recent Trends and Challenges in Computational Models, ICRTCCM 2017, pp 61\u201366. https:\/\/doi.org\/10.1109\/ICRTCCM.2017.84.","DOI":"10.1109\/ICRTCCM.2017.84"},{"issue":"9","key":"14925_CR50","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1109\/TSMC.2018.2850149","volume":"49","author":"A Kamel","year":"2019","unstructured":"Kamel A, Sheng B, Yang P, Li P, Shen R, Feng DD (2019) Deep convolutional neural networks for human action recognition using depth maps and postures. IEEE Trans Syst Man Cybern Syst 49(9):1806\u20131819. https:\/\/doi.org\/10.1109\/TSMC.2018.2850149","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"14925_CR51","doi-asserted-by":"publisher","unstructured":"Kim G, Kim J, Kim S (2019) \u201cFire Detection Using Video Images and Temporal Variations,\u201d 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, pp. 564\u2013567, https:\/\/doi.org\/10.1109\/ICAIIC.2019.8669083.","DOI":"10.1109\/ICAIIC.2019.8669083"},{"key":"14925_CR52","unstructured":"Koidan K (2018) New datasets for action recognition. https:\/\/neurohive.io\/en\/datasets\/new-datasets-for-action-recognition\/"},{"key":"14925_CR53","doi-asserted-by":"publisher","unstructured":"Koutras P, Zlatinsi A, Maragos P (2018) Exploring CNN-Based Architectures for Multimodal Salient Event Detection in Videos. 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2018 - Proceedings, pp 1\u20135, https:\/\/doi.org\/10.1109\/IVMSPW.2018.8448977.","DOI":"10.1109\/IVMSPW.2018.8448977"},{"key":"14925_CR54","unstructured":"Kushwaha A (2017) Theft-Detection using Motion Sensing Camera. 2(11):90\u201397"},{"issue":"2","key":"14925_CR55","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/s10044-017-0660-5","volume":"22","author":"Y Li","year":"2019","unstructured":"Li Y, Zhai Q, Ding S, Yang F, Li G, Zheng YF (2019) Efficient health-related abnormal behavior detection with visual and inertial sensor integration. Pattern Anal Applic 22(2):601\u2013614. https:\/\/doi.org\/10.1007\/s10044-017-0660-5","journal-title":"Pattern. Anal. Applic."},{"key":"14925_CR56","doi-asserted-by":"publisher","first-page":"107355","DOI":"10.1016\/j.patcog.2020.107355","volume":"108","author":"A Li","year":"2020","unstructured":"Li A, Miao Z, Cen Y, Zhang XP, Zhang L, Chen S (2020) Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning. Pattern Recogn 108:107355. https:\/\/doi.org\/10.1016\/j.patcog.2020.107355","journal-title":"Pattern Recogn."},{"key":"14925_CR57","doi-asserted-by":"publisher","unstructured":"Liu M, Yuan J (2018) Recognizing human actions as the evolution of pose estimation maps. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1159\u20131168. https:\/\/doi.org\/10.1109\/CVPR.2018.00127.","DOI":"10.1109\/CVPR.2018.00127"},{"key":"14925_CR58","doi-asserted-by":"publisher","unstructured":"Liu H, Feris R, Sun M (2011) Visual Analysis of Humans. Vis Anal Hum. https:\/\/doi.org\/10.1007\/978-0-85729-997-0.","DOI":"10.1007\/978-0-85729-997-0"},{"issue":"P2","key":"14925_CR59","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neucom.2014.04.090","volume":"151","author":"AA Liu","year":"2015","unstructured":"Liu AA, Xu N, Su YT, Lin H, Hao T, Yang ZX (2015) Single\/multi-view human action recognition via regularized multi-task learning. Neurocomputing 151(P2):544\u2013553. https:\/\/doi.org\/10.1016\/j.neucom.2014.04.090","journal-title":"Neurocomputing"},{"key":"14925_CR60","doi-asserted-by":"publisher","unstructured":"Luna E, Miguel JCS, Ortego D, Mart\u00ednez JM (2018) Abandoned object detection in video-surveillance: Survey and comparison. Sensors (Switzerland), vol. 18, no. 12, https:\/\/doi.org\/10.3390\/s18124290.","DOI":"10.3390\/s18124290"},{"key":"14925_CR61","unstructured":"Ma Y, Lu L, Zhang H, Li M (2002) A User Attention Model for Video Summarization. ACM, pp 1\u201310, [Online]. Available: papers2:\/\/publication\/uuid\/DE9F0C43-0DAB-459B-ADDC-928A1433801B"},{"key":"14925_CR62","doi-asserted-by":"publisher","unstructured":"Mabrouk AB, Zagrouba E (2018) Abnormal behavior recognition for intelligent video surveillance systems: a review. Exp Syst Appl 91:480\u2013491. https:\/\/doi.org\/10.1016\/j.eswa.2017.09.029","DOI":"10.1016\/j.eswa.2017.09.029"},{"key":"14925_CR63","doi-asserted-by":"publisher","unstructured":"Mahasseni B, Lam M, Todorovic S (2017) Unsupervised video summarization with adversarial LSTM networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp 2982\u20132991. https:\/\/doi.org\/10.1109\/CVPR.2017.318.","DOI":"10.1109\/CVPR.2017.318"},{"key":"14925_CR64","doi-asserted-by":"publisher","unstructured":"Mahesh Kini M, Pai K (2019) A Survey on Video Summarization Techniques. 2019 Innovations in Power and Advanced Computing Technologies, i-PACT 2019, pp 1\u20135. https:\/\/doi.org\/10.1109\/i-PACT44901.2019.8960003.","DOI":"10.1109\/i-PACT44901.2019.8960003"},{"key":"14925_CR65","doi-asserted-by":"publisher","unstructured":"Marvaniya S, Damoder M, Gopalakrishnan V, Iyer KN, Soni K (2016) Real-time video summarization on mobile. Proceedings - International Conference on Image Processing, ICIP, vol. 2016-Augus, no. September 2016, pp 176\u201318. https:\/\/doi.org\/10.1109\/ICIP.2016.7532342.","DOI":"10.1109\/ICIP.2016.7532342"},{"key":"14925_CR66","unstructured":"McCue T (2018) Video Marketing Trends (Forbes). https:\/\/www.forbes.com\/sites\/tjmccue\/2018\/06\/22\/video-marketing-2018-trends-continues-to-explode-as-the-way-to-reach-customers\/?sh=5fd70755598d"},{"key":"14925_CR67","doi-asserted-by":"publisher","unstructured":"Mei T, Tang LX, Tang J, Hua XS (2013) Near-lossless semantic video summarization and its applications to video analysis. ACM Trans Multimed Comput Commun Appl 9(3). https:\/\/doi.org\/10.1145\/2487268.2487269.","DOI":"10.1145\/2487268.2487269"},{"key":"14925_CR68","doi-asserted-by":"publisher","unstructured":"Milotta FLM, Furnari A, Battiato S, Signorello G, Farinella GM (2019) Egocentric visitors localization in natural sites. J Vis Commun Image Represent 65(2). https:\/\/doi.org\/10.1016\/j.jvcir.2019.102664.","DOI":"10.1016\/j.jvcir.2019.102664"},{"key":"14925_CR69","unstructured":"Mlik N, Barhoumi W, Zagrouba E (2014) Object-based event detection for the extraction of video key-frames (no. January 2012)"},{"issue":"March","key":"14925_CR70","doi-asserted-by":"publisher","first-page":"18174","DOI":"10.1109\/ACCESS.2018.2812835","volume":"6","author":"K Muhammad","year":"2018","unstructured":"Muhammad K, Ahmad J, Mehmood I, Rho S, Baik SW (2018) Convolutional Neural Networks Based Fire Detection in Surveillance Videos. IEEE Access 6(March):18174\u201318183. https:\/\/doi.org\/10.1109\/ACCESS.2018.2812835","journal-title":"IEEE Access"},{"issue":"7","key":"14925_CR71","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TSMC.2018.2830099","volume":"49","author":"K Muhammad","year":"2019","unstructured":"Muhammad K, Ahmad J, Lv Z, Bellavista P, Yang P, Baik SW (2019) Efficient deep CNN-based fire detection and localization in video surveillance applications. IEEE Trans Syst Man Cybern Syst 49(7):1419\u20131434. https:\/\/doi.org\/10.1109\/TSMC.2018.2830099","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1","key":"14925_CR72","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s11042-016-4219-z","volume":"77","author":"B M\u00fcnzer","year":"2018","unstructured":"M\u00fcnzer B, Schoeffmann K, B\u00f6sz\u00f6rmenyi L (2018) Content-based processing and analysis of endoscopic images and videos: a survey. Multimed Tools Appl 77(1):1323\u20131362. https:\/\/doi.org\/10.1007\/s11042-016-4219-z","journal-title":"Multimed. Tools Appl."},{"key":"14925_CR73","doi-asserted-by":"publisher","unstructured":"Muszynski M, Kostoulas T, Lombardo P, Pun T, Chanel G (2018) Aesthetic highlight detection in movies based on synchronization of spectators\u2019 reactions. ACM Trans Multimed Comput Commun Appl 14(3). https:\/\/doi.org\/10.1145\/3175497.","DOI":"10.1145\/3175497"},{"issue":"12","key":"14925_CR74","doi-asserted-by":"publisher","first-page":"2991","DOI":"10.1109\/TCYB.2015.2493558","volume":"46","author":"L Nie","year":"2016","unstructured":"Nie L, Hong R, Zhang L, Xia Y, Tao D, Sebe N (2016) Perceptual attributes optimization for multivideo summarization. IEEE Trans Cybern 46(12):2991\u20133003. https:\/\/doi.org\/10.1109\/TCYB.2015.2493558","journal-title":"IEEE Trans Cybern"},{"key":"14925_CR75","doi-asserted-by":"crossref","unstructured":"Oskouie P, Alipour S, Eftekhari-Moghadam AM (2014) Multimodal feature extraction and fusion for semantic mining of soccer video: a survey. Artif Intell Rev 42(2):173\u2013210","DOI":"10.1007\/s10462-012-9332-4"},{"key":"14925_CR76","doi-asserted-by":"publisher","unstructured":"Pareek P, Thakkar A (2021) A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications, vol. 54, no. 3. Springer Netherlands. https:\/\/doi.org\/10.1007\/s10462-020-09904-8.","DOI":"10.1007\/s10462-020-09904-8"},{"key":"14925_CR77","doi-asserted-by":"publisher","unstructured":"Park H, Park S, Joo Y (2019) Robust detection of abandoned object for smart video surveillance in illumination changes. Sensors (Switzerland), vol. 19, no. 23, https:\/\/doi.org\/10.3390\/s19235114.","DOI":"10.3390\/s19235114"},{"key":"14925_CR78","doi-asserted-by":"publisher","first-page":"80010","DOI":"10.1109\/ACCESS.2020.2990618","volume":"8","author":"H Park","year":"2020","unstructured":"Park H, Park S, Joo Y (2020) Detection of abandoned and stolen objects based on dual background model and mask R-CNN. IEEE Access 8:80010\u201380019. https:\/\/doi.org\/10.1109\/ACCESS.2020.2990618","journal-title":"IEEE Access"},{"key":"14925_CR79","doi-asserted-by":"publisher","unstructured":"Plummer BA, Brown M, Lazebnik S (2017) Enhancing video summarization via vision-language embedding. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp 1052\u20131060. https:\/\/doi.org\/10.1109\/CVPR.2017.118.","DOI":"10.1109\/CVPR.2017.118"},{"issue":"6","key":"14925_CR80","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1109\/JBHI.2019.2942845","volume":"24","author":"PV Rouast","year":"2020","unstructured":"Rouast PV, Adam MTP (2020) Learning deep representations for video-based intake gesture detection. IEEE J Biomed Health Inf 24(6):1727\u20131737. https:\/\/doi.org\/10.1109\/JBHI.2019.2942845","journal-title":"IEEE J Biomed Health Inf"},{"key":"14925_CR81","doi-asserted-by":"publisher","unstructured":"Rouvier M, Oger S, Linar\u00e8s G, Matrouf D, Merialdo B, Li Y (2015) Audio-based video genre identification. IEEE Trans. Audio Speech Lang Process 23(6):1031\u20131041. https:\/\/doi.org\/10.1109\/TASLP.2014.2387411","DOI":"10.1109\/TASLP.2014.2387411"},{"key":"14925_CR82","doi-asserted-by":"publisher","unstructured":"Sabha A, Selwal A (2021) HAVS: Human action-based video summarization, Taxonomy, Challenges, and Future Perspectives. Proceedings of the 2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021, pp 1\u20139. https:\/\/doi.org\/10.1109\/ICSES52305.2021.9633804.","DOI":"10.1109\/ICSES52305.2021.9633804"},{"key":"14925_CR83","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.patrec.2020.02.029","volume":"133","author":"A Sahu","year":"2020","unstructured":"Sahu A, Chowdhury AS (2020) Multiscale summarization and action ranking in egocentric videos. Pattern Recogn Lett 133:256\u2013263. https:\/\/doi.org\/10.1016\/j.patrec.2020.02.029","journal-title":"Pattern Recogn. Lett."},{"issue":"s5","key":"14925_CR84","doi-asserted-by":"publisher","first-page":"10577","DOI":"10.1007\/s10586-017-1131-x","volume":"22","author":"KP Sanal Kumar","year":"2019","unstructured":"Sanal Kumar KP, Bhavani R (2019) Human activity recognition in egocentric video using PNN, SVM, kNN and SVM+kNN classifiers. Clust Comput 22(s5):10577\u201310586. https:\/\/doi.org\/10.1007\/s10586-017-1131-x","journal-title":"Clust. Comput."},{"key":"14925_CR85","unstructured":"Sarika (2022) 135 Video Marketing Statistics You Can\u2019t Ignore in 2022. https:\/\/invideo.io\/blog\/video-marketing-statistics\/"},{"key":"14925_CR86","unstructured":"Savage C (2016) Does length matter? It does for video!. https:\/\/wistia.com\/learn\/marketing\/does-length-matter-it-does-for-video"},{"key":"14925_CR87","doi-asserted-by":"crossref","unstructured":"Schuldt C, Barbara L, Stockholm S (2004) Recognizing human actions: a local SVM approach \u2217 Dept. of Numerical Analysis and Computer Science. Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th international conference on, vol. 3, pp 32\u201336","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"14925_CR88","doi-asserted-by":"publisher","unstructured":"Vivekraj VK, Debashis S, Balasubramanian R (2019) Video Skimming: taxonomy and comprehensive survey. ACM Comput Surv 52(5):(Article 106)38. https:\/\/doi.org\/10.1145\/3347712","DOI":"10.1145\/3347712"},{"key":"14925_CR89","doi-asserted-by":"publisher","unstructured":"Shammi S, Islam S, Rahman HA, Zaman HU (2019) An automated way of vehicle theft detection in parking facilities by identifying moving vehicles in CCTV video stream. Proceedings of the 2018 International Conference On Communication, Computing and Internet of Things, IC3IoT 2018, pp 36\u201341. https:\/\/doi.org\/10.1109\/IC3IoT.2018.8668135","DOI":"10.1109\/IC3IoT.2018.8668135"},{"key":"14925_CR90","doi-asserted-by":"publisher","unstructured":"Shang X, Yuan Z, Wang A, Wang C (2021) Multimodal video summarization via time-aware transformers. MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia, pp. 1756\u20131765. https:\/\/doi.org\/10.1145\/3474085.3475321","DOI":"10.1145\/3474085.3475321"},{"key":"14925_CR91","doi-asserted-by":"publisher","unstructured":"Sharma D, Selwal A (2021) HyFiPAD: a hybrid approach for fingerprint presentation attack detection using local and adaptive image features. Vis Comput no. 0123456789, https:\/\/doi.org\/10.1007\/s00371-021-02173-8.","DOI":"10.1007\/s00371-021-02173-8"},{"key":"14925_CR92","doi-asserted-by":"publisher","unstructured":"Sharma D, Selwal A (2021) An intelligent approach for fingerprint presentation attack detection using ensemble learning with improved local image features, no. 0123456789. Springer US, https:\/\/doi.org\/10.1007\/s11042-021-11254-8.","DOI":"10.1007\/s11042-021-11254-8"},{"issue":"April 2020","key":"14925_CR93","doi-asserted-by":"publisher","first-page":"102991","DOI":"10.1016\/j.jvcir.2020.102991","volume":"74","author":"A Singh Parihar","year":"2021","unstructured":"Singh Parihar A, Pal J, Sharma I (2021) Multiview video summarization using video partitioning and clustering. J Vis Commun Image Represent 74(April 2020):102991. https:\/\/doi.org\/10.1016\/j.jvcir.2020.102991","journal-title":"J Vis Commun Image Represent"},{"key":"14925_CR94","doi-asserted-by":"publisher","unstructured":"Singh T, Vishwakarma DK (2021) A deeply coupled ConvNet for human activity recognition using dynamic and RGB images. Neural Comput Applic 33(1):469\u2013485. https:\/\/doi.org\/10.1007\/s00521-020-05018-y","DOI":"10.1007\/s00521-020-05018-y"},{"key":"14925_CR95","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.neucom.2015.07.131","volume":"187","author":"X Song","year":"2016","unstructured":"Song X, Sun L, Lei J, Tao D, Yuan G, Song M (2016) Event-based large scale surveillance video summarization. Neurocomputing 187:66\u201374. https:\/\/doi.org\/10.1016\/j.neucom.2015.07.131","journal-title":"Neurocomputing"},{"key":"14925_CR96","unstructured":"Sood M (2020) The Hindustan Times. https:\/\/www.hindustantimes.com\/mumbai-news\/india-had-most-deaths-in-road-accidents-in-2019-report\/story-pikRXxsS4hptNVvf6J2g9O.html#:~:text=India.continued to have the,in 2019%2C the report revealed"},{"issue":"1","key":"14925_CR97","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/0893-6080(90)90049-Q","volume":"3","author":"DF Specht","year":"1990","unstructured":"Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109\u2013118. https:\/\/doi.org\/10.1016\/0893-6080(90)90049-Q","journal-title":"Neural Netw."},{"issue":"2019","key":"14925_CR98","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1016\/j.procs.2020.03.203","volume":"167","author":"M Sridevi","year":"2020","unstructured":"Sridevi M, Kharde M (2020) Video summarization using highlight detection and pairwise deep ranking model. Procedia Comput Sci 167(2019):1839\u20131848. https:\/\/doi.org\/10.1016\/j.procs.2020.03.203","journal-title":"Procedia Comput Sci"},{"key":"14925_CR99","doi-asserted-by":"publisher","unstructured":"Srivastava AK, Biswas KK (2018) Human activity recognition using local motion histogram. In: Bhattacharyya P, Sastry H, Marriboyina V, Sharma R (eds), Smart and innovative trends in next generation computing technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-8660-1_69","DOI":"10.1007\/978-981-10-8660-1_69"},{"key":"14925_CR100","unstructured":"Staff R (2020) Video marketing statistics 2021 [infographic]. https:\/\/www.renderforest.com\/blog\/video-marketing-statistics"},{"key":"14925_CR101","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6479\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"issue":"7","key":"14925_CR102","doi-asserted-by":"publisher","first-page":"9093","DOI":"10.1007\/s11042-017-4807-6","volume":"77","author":"S Sun","year":"2018","unstructured":"Sun S, Wang F, He L (2018) Movie summarization using bullet screen comments. Multimed Tools Appl 77(7):9093\u20139110. https:\/\/doi.org\/10.1007\/s11042-017-4807-6","journal-title":"Multimed. Tools Appl."},{"key":"14925_CR103","doi-asserted-by":"publisher","unstructured":"Tabish M, Tanooli ZUR, Shaheen M (2021) Activity recognition framework in sports videos. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-021-10519-6.","DOI":"10.1007\/s11042-021-10519-6"},{"key":"14925_CR104","doi-asserted-by":"crossref","unstructured":"Tang K, Bao Y, Zhao Z, Zhu L, Lin Y, Peng Y (2019) AutoHighlight: automatic highlights detection and segmentation in soccer matches. In 2018 IEEE International Conference on Big Data (Big Data), pp 4619\u20134624. IEEE.","DOI":"10.1109\/BigData.2018.8621906"},{"key":"14925_CR105","unstructured":"Terms I (2015) A multi-view video synopsis framework Ansuman Mahapatra, Pankaj K Sa, and Banshidhar Majhi Department of Computer Science and Engineering National Institute of Technology Rourkela. Int Conf Image Process (ICIP), pp 1\u20135"},{"key":"14925_CR106","doi-asserted-by":"publisher","unstructured":"Tian Z, Xue J, Lan X, Li C, Zheng N (2011) Key object-based static video summarization. MM\u201911 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, pp 1301\u20131304. https:\/\/doi.org\/10.1145\/2072298.2071999.","DOI":"10.1145\/2072298.2071999"},{"key":"14925_CR107","doi-asserted-by":"publisher","unstructured":"Tian Z, Xue J, Lan X, Li C, Zheng N (2014) Object segmentation and key-pose based summarization for motion video. Multimed. Tools Appl 72(2):1773\u20131802. https:\/\/doi.org\/10.1007\/s11042-013-1488-7","DOI":"10.1007\/s11042-013-1488-7"},{"key":"14925_CR108","unstructured":"Tribune T (2022) Rash driving to blame for 92% accidents in 2019-road crash analysis cell report. https:\/\/www.tribuneindia.com\/news\/chandigarh\/rash-driving-to-blame-for-92-accidents-in-2019-114422.Accessed 18 Jul 2020"},{"issue":"2","key":"14925_CR109","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s10462-017-9545-7","volume":"50","author":"RK Tripathi","year":"2018","unstructured":"Tripathi RK, Jalal AS, Agrawal SC (2018) Suspicious human activity recognition: a review. Artif Intell Rev 50(2):283\u2013339. https:\/\/doi.org\/10.1007\/s10462-017-9545-7","journal-title":"Artif. Intell. Rev."},{"key":"14925_CR110","doi-asserted-by":"publisher","unstructured":"Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimed Comput Commun Appl 3(1):3-es. https:\/\/doi.org\/10.1145\/1198302.1198305","DOI":"10.1145\/1198302.1198305"},{"key":"14925_CR111","doi-asserted-by":"crossref","unstructured":"Uemura H, Ishikawa S, Mikolajczyk K (2008) Feature tracking and motion compensation for action recognition. In BMVC, pp 1\u201310","DOI":"10.5244\/C.22.30"},{"key":"14925_CR112","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","volume":"6","author":"A Ullah","year":"2017","unstructured":"Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2017) Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6:1155\u20131166. https:\/\/doi.org\/10.1109\/ACCESS.2017.2778011","journal-title":"IEEE Access"},{"key":"14925_CR113","unstructured":"Vaswani A et al. (2017) Attention is all you need. Adv Neural Inf Process Syst, vol. 2017-Decem, no. Nips, pp 5999\u20136009"},{"key":"14925_CR114","doi-asserted-by":"publisher","unstructured":"Verma KK, Singh BM, Dixit A (2019) A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system. Int J Inf Technol pp 1\u201314. https:\/\/doi.org\/10.1007\/s41870-019-00364-0.","DOI":"10.1007\/s41870-019-00364-0"},{"issue":"10","key":"14925_CR115","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s00371-012-0752-6","volume":"29","author":"S Vishwakarma","year":"2013","unstructured":"Vishwakarma S, Agrawal A (2013) A survey on activity recognition and behavior understanding in video surveillance. Vis Comput 29(10):983\u20131009. https:\/\/doi.org\/10.1007\/s00371-012-0752-6","journal-title":"Vis. Comput."},{"issue":"1","key":"14925_CR116","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/TMM.2011.2165531","volume":"14","author":"F Wang","year":"2012","unstructured":"Wang F, Ngo CW (2012) Summarizing rushes videos by motion, object, and event understanding. IEEE Trans Multimed 14(1):76\u201387. https:\/\/doi.org\/10.1109\/TMM.2011.2165531","journal-title":"IEEE Trans Multimed"},{"key":"14925_CR117","doi-asserted-by":"publisher","unstructured":"Wang T, Chen J, Snoussi H (2013) Online detection of abnormal events in video streams. J Electr Comput Eng 2013, https:\/\/doi.org\/10.1155\/2013\/837275.","DOI":"10.1155\/2013\/837275"},{"key":"14925_CR118","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","volume":"119","author":"J Wang","year":"2019","unstructured":"Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: a survey. Pattern Recogn Lett 119:3\u201311. https:\/\/doi.org\/10.1016\/j.patrec.2018.02.010","journal-title":"Pattern Recogn. Lett."},{"key":"14925_CR119","unstructured":"World Health Organization (2018) Global status report on road safety 2018. https:\/\/www.who.int\/publications\/i\/item\/9789241565684"},{"issue":"6","key":"14925_CR120","doi-asserted-by":"publisher","first-page":"6955","DOI":"10.1007\/s11042-017-4614-0","volume":"77","author":"Q Xiao","year":"2018","unstructured":"Xiao Q, Song R (2018) Action recognition based on hierarchical dynamic Bayesian network. Multimed Tools Appl 77(6):6955\u20136968. https:\/\/doi.org\/10.1007\/s11042-017-4614-0","journal-title":"Multimed. Tools Appl."},{"key":"14925_CR121","doi-asserted-by":"crossref","unstructured":"Xu L, Yan S, Chen X, Wang P (2019) Motion recognition algorithm based on deep edge-aware pyramid pooling network in human-computer interaction. IEEE Access 7:163806\u2013163813","DOI":"10.1109\/ACCESS.2019.2952432"},{"issue":"4","key":"14925_CR122","doi-asserted-by":"publisher","first-page":"6121","DOI":"10.1007\/s11042-020-09888-1","volume":"80","author":"J Xu","year":"2021","unstructured":"Xu J, Sun Z, Ma C (2021) Crowd aware summarization of surveillance videos by deep reinforcement learning. Multimed. Tools Appl. 80(4):6121\u20136141. https:\/\/doi.org\/10.1007\/s11042-020-09888-1","journal-title":"Multimed. Tools Appl."},{"key":"14925_CR123","doi-asserted-by":"publisher","unstructured":"Yasmin G, Chowdhury S, Nayak J, Das P, Das AK (2021) Key moment extraction for designing an agglomerative clustering algorithm-based video summarization framework. Neural Comput Appl, vol. 1, https:\/\/doi.org\/10.1007\/s00521-021-06132-1.","DOI":"10.1007\/s00521-021-06132-1"},{"key":"14925_CR124","doi-asserted-by":"publisher","first-page":"107396","DOI":"10.1016\/j.patcog.2020.107396","volume":"106","author":"DH Yoon","year":"2020","unstructured":"Yoon DH, Cho NG, Lee SW (2020) A novel online action detection framework from untrimmed video streams. Pattern Recogn 106:107396. https:\/\/doi.org\/10.1016\/j.patcog.2020.107396","journal-title":"Pattern Recogn."},{"key":"14925_CR125","doi-asserted-by":"publisher","unstructured":"Zhang Y, Zhang L, Zimmermann R (2014) Aesthetics-guided summarization from multiple user generated videos. ACM Trans Multimed Comput Commun Appl 11(2). https:\/\/doi.org\/10.1145\/2659520.","DOI":"10.1145\/2659520"},{"key":"14925_CR126","doi-asserted-by":"publisher","unstructured":"Zhang B, Conci N, de Natale FGB (2015) Segmentation of discriminative patches in human activity video. ACM Trans Multimed Comput Commun Appl 12(1):1\u201319. https:\/\/doi.org\/10.1145\/2750780.","DOI":"10.1145\/2750780"},{"key":"14925_CR127","doi-asserted-by":"publisher","unstructured":"Zhang Z et al. (2019) Multi-scale visualization based on sketch interaction for massive surveillance video data. Pers Ubiquit Comput. https:\/\/doi.org\/10.1007\/s00779-019-01281-6.","DOI":"10.1007\/s00779-019-01281-6"},{"key":"14925_CR128","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.patrec.2018.07.030","volume":"130","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Liang X, Zhang D, Tan M, Xing EP (2020) Unsupervised object-level video summarization with online motion auto-encoder. Pattern Recogn Lett 130:376\u2013385. https:\/\/doi.org\/10.1016\/j.patrec.2018.07.030","journal-title":"Pattern Recogn. Lett."},{"key":"14925_CR129","doi-asserted-by":"publisher","unstructured":"Zhao B, Li X, Lu X (2018) HSA-RNN: hierarchical structure-adaptive RNN for video summarization. Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 7405\u20137414, https:\/\/doi.org\/10.1109\/CVPR.2018.00773.","DOI":"10.1109\/CVPR.2018.00773"},{"key":"14925_CR130","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.neucom.2021.10.039","volume":"468","author":"B Zhao","year":"2022","unstructured":"Zhao B, Gong M, Li X (2022) Hierarchical multimodal transformer to summarize videos. Neurocomputing 468:360\u2013369. https:\/\/doi.org\/10.1016\/j.neucom.2021.10.039","journal-title":"Neurocomputing"},{"key":"14925_CR131","doi-asserted-by":"crossref","unstructured":"Zhou K, Qiao Y, Xiang T (2018) Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp 7582\u20137589","DOI":"10.1609\/aaai.v32i1.12255"},{"key":"14925_CR132","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.imavis.2016.06.007","volume":"55","author":"F Zhu","year":"2016","unstructured":"Zhu F, Shao L, Xie J, Fang Y (2016) From handcrafted to learned representations for human action recognition: a survey. Image Vis Comput 55:42\u201352. https:\/\/doi.org\/10.1016\/j.imavis.2016.06.007","journal-title":"Image Vis. Comput."},{"key":"14925_CR133","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1109\/TIP.2020.3039886","volume":"30","author":"W Zhu","year":"2021","unstructured":"Zhu W, Lu J, Li J, Zhou J (2021) DSNet: a flexible detect-to-summarize network for video summarization. IEEE Trans Image Process 30:948\u2013962. https:\/\/doi.org\/10.1109\/TIP.2020.3039886","journal-title":"IEEE Trans. Image Process."},{"issue":"94","key":"14925_CR134","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1109\/icip.1998.723655","volume":"1","author":"Y Zhuang","year":"1998","unstructured":"Zhuang Y, Rui Y, Huang TS, Mehrotra S (1998) Adaptive key frame extraction using unsupervised clustering. IEEE Int Conf Image Process 1(94):866\u2013870. https:\/\/doi.org\/10.1109\/icip.1998.723655","journal-title":"IEEE Int Conf Image Process"},{"key":"14925_CR135","unstructured":"Zutshi A, Gupta A, Raj A (2021) TRACS Transformer for Video Captioning and Summarisation TRACS: transformer for Video Captioning and Summarisation (no. January)"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14925-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14925-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14925-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T09:28:24Z","timestamp":1693474104000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14925-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,2]]},"references-count":135,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["14925"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14925-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,2]]},"assertion":[{"value":"22 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that they do not have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}