{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T05:26:58Z","timestamp":1768541218747,"version":"3.49.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","award":["ECR\/2016\/000387"],"award-info":[{"award-number":["ECR\/2016\/000387"]}],"id":[{"id":"10.13039\/501100001843","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":[[2022,9]]},"DOI":"10.1007\/s11042-022-12656-y","type":"journal-article","created":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T03:26:48Z","timestamp":1649993208000},"page":"32857-32881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Online suspicious event detection in a constrained environment with RGB+D camera using multi-stream CNNs and SVM"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4065-4338","authenticated-orcid":false,"given":"Pushpajit","family":"Khaire","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praveen","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"issue":"3","key":"12656_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/1922649.1922653","volume":"43","author":"JK Aggarwal","year":"2011","unstructured":"Aggarwal JK, Ryoo MS (2011) Human activity analysis: A review. ACM Computing Surveys (CSUR) 43(3):16","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"12656_CR2","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.patrec.2014.04.011","volume":"48","author":"JK Aggarwal","year":"2014","unstructured":"Aggarwal JK, Xia L (2014) Human activity recognition from 3d data: A review. Pattern Recogn Lett 48:70\u201380","journal-title":"Pattern Recogn Lett"},{"key":"12656_CR3","doi-asserted-by":"crossref","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: IEEE international conference on image processing (ICIP). IEEE, pp 168\u2013172","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"12656_CR4","unstructured":"Chen Y, Wang L, Li C, Hou Y, Li W (2019) ConvNets-based action recognition from skeleton motion maps. Multimedia Tools and Applications, pp 1\u201319"},{"key":"12656_CR5","doi-asserted-by":"publisher","first-page":"21","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. Computational Intelligence and Neuroscience 2016:21","journal-title":"Computational Intelligence and Neuroscience"},{"key":"12656_CR6","doi-asserted-by":"crossref","unstructured":"Du Y, Fu Y, Wang L (2015) Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR asian conference on pattern recognition (ACPR). IEEE, pp 579\u2013583","DOI":"10.1109\/ACPR.2015.7486569"},{"key":"12656_CR7","unstructured":"Hou Y, Li Z, Wang P, Li W (2016) Skeleton optical spectra based action recognition using convolutional neural networks. IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"12656_CR8","doi-asserted-by":"crossref","unstructured":"Hu J-F, Zheng W-S, Lai J, Zhang J (2015) Jointly learning heterogeneous featuresfor rgb-d activity recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5344\u20135352","DOI":"10.1109\/CVPR.2015.7299172"},{"key":"12656_CR9","doi-asserted-by":"crossref","unstructured":"Huynh-The T, Hua-Cam H, Kim D-S (2019) Encoding pose features to images with data augmentation for 3D action recognition. IEEE Transactions on Industrial Informatics","DOI":"10.1109\/TII.2019.2910876"},{"key":"12656_CR10","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.patcog.2017.07.013","volume":"72","author":"EP Ijjina","year":"2017","unstructured":"Ijjina EP, Chalavadi KM (2017) Human action recognition in rgb-d videos using motion sequence information and deep learning. Pattern Recogn 72:504\u2013516","journal-title":"Pattern Recogn"},{"key":"12656_CR11","doi-asserted-by":"crossref","unstructured":"Imran J, Kumar P (2016) Human action recognition using rgb-d sensor and deep convolutional neural networks. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 144\u2013148","DOI":"10.1109\/ICACCI.2016.7732038"},{"issue":"9","key":"12656_CR12","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1109\/TSMC.2018.2850149","volume":"49","author":"A Kamel","year":"2018","unstructured":"Kamel A, Sheng B, Yang P, Li P, Shen R, Feng DD (2018) Deep convolutional neural networks for human action recognition using depth maps and postures. IEEE Trans Syst Man Cybern Syst 49(9):1806\u20131819","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"12656_CR13","unstructured":"Karg M, Kirsch A (2014) A human morning routine dataset. In: 13th international conference on autonomous agents and multiagent systems"},{"key":"12656_CR14","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, FeiFei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"12656_CR15","doi-asserted-by":"crossref","unstructured":"Khaire P, Imran J, Kumar P (2018) Human activity recognition by fusion of rgb, depth, and skeletal data. In: Proceedings of 2nd international conference on computer vision & image processing. Springer, pp 409\u2013421","DOI":"10.1007\/978-981-10-7895-8_32"},{"key":"12656_CR16","doi-asserted-by":"crossref","unstructured":"Khaire P, Kumar P, Imran J (2018) Combining cnn streams of rgb-d and skeletal data for human activity recognition. Pattern Recogn Lett, pp 107\u2013116","DOI":"10.1016\/j.patrec.2018.04.035"},{"issue":"8","key":"12656_CR17","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1177\/0278364913478446","volume":"32","author":"HS Koppula","year":"2013","unstructured":"Koppula HS, Gupta R, Saxena A (2013) Learning human activities and object affordances from rgb-d videos. The International Journal of Robotics Research 32(8):951\u2013970","journal-title":"The International Journal of Robotics Research"},{"issue":"5","key":"12656_CR18","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/LSP.2017.2678539","volume":"24","author":"C Li","year":"2017","unstructured":"Li C, Hou Y, Wang P, Li W (2017) Joint distance maps based action recognition with convolutional neural networks. IEEE Signal Process Lett 24(5):624\u2013628","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"12656_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TPAMI.2013.111","volume":"36","author":"W Li","year":"2014","unstructured":"Li W, Mahadevan V, Vasconcelos N (2014) Anomaly detection and localization in crowded scenes. IEEE Trans Pattern Anal Mach Intell 36(1):18\u201332","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"12656_CR20","doi-asserted-by":"crossref","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 workshops (CVPRW). IEEE, pp 9\u201314","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"12656_CR21","doi-asserted-by":"crossref","unstructured":"Liu M, Meng F, Chen C, Songtao W (2019) Joint dynamic pose image and space time reversal for human action recognition from videos. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8762\u20138769","DOI":"10.1609\/aaai.v33i01.33018762"},{"key":"12656_CR22","doi-asserted-by":"crossref","unstructured":"Liu F, Tang J, Zhao R, Tang Z (2012) Abnormal behavior recognition system for atm monitoring by rgb-d camera. In: Proceedings of the 20th ACM international conference on Multimedia. ACM, pp 1295\u20131296","DOI":"10.1145\/2393347.2396450"},{"key":"12656_CR23","doi-asserted-by":"crossref","unstructured":"Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE international conference on computer vision, pp 2720\u20132727","DOI":"10.1109\/ICCV.2013.338"},{"issue":"4","key":"12656_CR24","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TSMCB.2012.2226879","volume":"43","author":"A Mansur","year":"2013","unstructured":"Mansur A, Makihara Y, Yagi Y (2013) Inverse dynamics for action recognition. IEEE Trans Cybern 43(4):1226\u20131236","journal-title":"IEEE Trans Cybern"},{"key":"12656_CR25","doi-asserted-by":"crossref","unstructured":"McNally W, Wong A, McPhee J (2019) STAR-Net: Action recognition using spatio-temporal activation reprojection. In: 2019 16th conference on computer and robot vision (CRV). IEEE, pp 49\u201356","DOI":"10.1109\/CRV.2019.00015"},{"key":"12656_CR26","doi-asserted-by":"crossref","unstructured":"Nar R, Singal A, Kumar P (2016) Abnormal activity detection for bank atm surveillance. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 2042\u20132046","DOI":"10.1109\/ICACCI.2016.7732351"},{"key":"12656_CR27","doi-asserted-by":"crossref","unstructured":"Nirjon S, Greenwood C, Torres C, Zhou S, Stankovic JA, Yoon HJ, Ra H.-K., Basaran C, Park T, Son SH (2013) Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3d skeleton data. In: Proceedings of the 11th ACM conference on embedded networked sensor systems. ACM, p 75","DOI":"10.1145\/2517351.2517396"},{"key":"12656_CR28","doi-asserted-by":"crossref","unstructured":"Ofli F, Chaudhry R, Kurillo G, Vidal R, Bajcsy R (2013) Berkeley mhad: A comprehensive multimodal human action database. In: 2013 IEEE workshop on applications of computer vision (WACV). IEEE, pp 53\u201360","DOI":"10.1109\/WACV.2013.6474999"},{"key":"12656_CR29","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng T.-T., Wang G (2016) Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1010\u20131019","DOI":"10.1109\/CVPR.2016.115"},{"key":"12656_CR30","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568\u2013576"},{"key":"12656_CR31","unstructured":"Sung J, Ponce C, Selman B, Saxena A (2011) Human activity detection from rgbd images. In: Workshops at the twenty-fifth AAAI conference on artificial intelligence"},{"issue":"1","key":"12656_CR32","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jvcir.2013.03.008","volume":"25","author":"I Theodorakopoulos","year":"2014","unstructured":"Theodorakopoulos I, Kastaniotis D, Economou G, Fotopoulos S (2014) Posebased human action recognition via sparse representation in dissimilarity space. J Vis Commun Image Represent 25(1):12\u201323","journal-title":"J Vis Commun Image Represent"},{"key":"12656_CR33","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.patrec.2017.04.004","volume":"92","author":"L Wang","year":"2017","unstructured":"Wang L, Ge L, Li R, Fang Y (2017) Three-stream cnns for action recognition. Pattern Recogn Lett 92:33\u201340","journal-title":"Pattern Recogn Lett"},{"key":"12656_CR34","doi-asserted-by":"crossref","unstructured":"Wang P, Li W, Gao Z, Tang C, Zhang J, Ogunbona P (2015) Convnets-based action recognition from depth maps through virtual cameras and pseudocoloring. In: Proceedings of the 23rd ACM international conference on Multimedia, pp 1119\u20131122","DOI":"10.1145\/2733373.2806296"},{"issue":"4","key":"12656_CR35","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/THMS.2015.2504550","volume":"46","author":"P Wang","year":"2015","unstructured":"Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona PO (2015) Action recognition from depth maps using deep convolutional neural networks. IEEE Transactions on Human-Machine Systems 46(4):498\u2013509","journal-title":"IEEE Transactions on Human-Machine Systems"},{"issue":"4","key":"12656_CR36","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/THMS.2015.2504550","volume":"46","author":"P Wang","year":"2016","unstructured":"Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona PO (2016) Action recognition from depth maps using deep convolutional neural networks. IEEE Transactions on Human-Machine Systems 46(4):498\u2013509","journal-title":"IEEE Transactions on Human-Machine Systems"},{"key":"12656_CR37","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.knosys.2018.05.029","volume":"158","author":"P Wang","year":"2018","unstructured":"Wang P, Li W, Li C, Hou Y (2018) Action recognition based on joint trajectory maps with convolutional neural networks. Knowl-Based Syst 158:43\u201353","journal-title":"Knowl-Based Syst"},{"key":"12656_CR38","doi-asserted-by":"crossref","unstructured":"Wang P, Li W, Ogunbona P, Wan J, Escalera S (2018) Rgb-d-based human motion recognition with deep learning: A survey, Computer Vision and Image Understanding","DOI":"10.1016\/j.cviu.2018.04.007"},{"key":"12656_CR39","doi-asserted-by":"crossref","unstructured":"Yun K, Honorio J, Chattopadhyay D, Berg TL, Samaras D (2012) Twoperson interaction detection using body-pose features and multiple instance learning. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 28\u201335","DOI":"10.1109\/CVPRW.2012.6239234"},{"key":"12656_CR40","doi-asserted-by":"crossref","unstructured":"Zhang S, Liu X, Xiao J (2017) On geometric features for skeleton-based action recognition using multilayer lstm networks. In: 2017 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 148\u2013157","DOI":"10.1109\/WACV.2017.24"},{"issue":"12","key":"12656_CR41","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.3390\/electronics8121511","volume":"8","author":"E Zhang","year":"2019","unstructured":"Zhang E, Xue B, Cao F, Duan J, Lin G, Lei Y (2019) Fusion of 2D CNN and 3D DenseNet for dynamic gesture recognition. Electronics 8 (12):1511","journal-title":"Electronics"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12656-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12656-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12656-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T06:08:50Z","timestamp":1661148530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12656-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,15]]},"references-count":41,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["12656"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12656-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,15]]},"assertion":[{"value":"9 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}