{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T06:44:43Z","timestamp":1785912283959,"version":"3.56.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11042-021-11438-2","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T07:04:31Z","timestamp":1631516671000},"page":"22497-22525","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An expert video surveillance system to identify and mitigate shoplifting in megastores"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2778-8339","authenticated-orcid":false,"given":"Mohd. Aquib","family":"Ansari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dushyant Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"issue":"3","key":"11438_CR1","doi-asserted-by":"publisher","first-page":"1","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):1\u201343","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"11438_CR2","doi-asserted-by":"crossref","unstructured":"Agarwal A, GuptaS, Singh DK (2016) Review of optical flow technique for moving object detection. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE","DOI":"10.1109\/IC3I.2016.7917999"},{"key":"11438_CR3","unstructured":"Ansari MA, Singh DK An Expert Eye for Identifying Shoplifters in Mega Stores. 4TH International Conference on Innovative Computing and Communication (ICICC 2021), Shaheed Sukhdev College of Business Studies, University of Delhi, New Delhi, 20\u201321st February, 2021."},{"issue":"21","key":"11438_CR4","doi-asserted-by":"publisher","first-page":"7991","DOI":"10.1016\/j.eswa.2015.06.016","volume":"42","author":"R Arroyo","year":"2015","unstructured":"Arroyo R et al (2015) Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Syst Appl 42(21):7991\u20138005","journal-title":"Expert Syst Appl"},{"key":"11438_CR5","doi-asserted-by":"crossref","unstructured":"Donahue J, et al (2015) Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"11438_CR6","volume-title":"Scandinavian conference on Image analysis","author":"G Farneb\u00e4ck","year":"2003","unstructured":"Farneb\u00e4ck G (2003) Two-frame motion estimation based on polynomial expansion. In: Farneb\u00e4ck G (ed) Scandinavian conference on Image analysis. Springer, Berlin, Heidelberg"},{"key":"11438_CR7","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.213"},{"issue":"13","key":"11438_CR8","doi-asserted-by":"publisher","first-page":"19361","DOI":"10.1007\/s11042-020-10435-1","volume":"80","author":"M Gholamrezaii","year":"2021","unstructured":"Gholamrezaii M, AlModarresi SMT (2021) A time-efficient convolutional neural network model in human activity recognition. Multimed Tools Appl 80(13):19361\u201319376","journal-title":"Multimed Tools Appl"},{"key":"11438_CR9","unstructured":"Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems. Advances in neural information processing systems 473\u2013479"},{"key":"11438_CR10","unstructured":"Ibrahim N, et al (2012) Detection of snatch theft based on temporal differences in motion flow field orientation histograms. Int J Adv Comput Technol 4(12)"},{"issue":"1","key":"11438_CR11","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2012","unstructured":"Ji S et al (2012) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11438_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-08525-w","author":"NS Khan","year":"2021","unstructured":"Khan NS, Ghani MS (2021) A Survey of deep learning based models for human activity recognition. Wireless Pers Commun. https:\/\/doi.org\/10.1007\/s11277-021-08525-w","journal-title":"Wireless Pers Commun"},{"issue":"5","key":"11438_CR13","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1007\/s11042-018-6034-1","volume":"79","author":"KPS Kumar","year":"2020","unstructured":"Kumar KPS, Bhavani R (2020) Human activity recognition in egocentric video using HOG, GiST and color features. Multimed Tools Appl 79(5):3543\u20133559","journal-title":"Multimed Tools Appl"},{"key":"11438_CR14","doi-asserted-by":"publisher","DOI":"10.1142\/S0219467822500097","author":"Arati Kushwaha","year":"2021","unstructured":"Kushwaha Arati, Khare Ashish, Khare Manish (2021) Human activity recognition algorithm in video sequences based on integration of magnitude and orientation information of optical flow. Int J Image Grap. https:\/\/doi.org\/10.1142\/S0219467822500097","journal-title":"Int J Image Grap"},{"issue":"21","key":"11438_CR15","doi-asserted-by":"publisher","first-page":"16387","DOI":"10.1007\/s00521-018-3951-x","volume":"32","author":"A Ladjailia","year":"2020","unstructured":"Ladjailia A et al (2020) Human activity recognition via optical flow: decomposing activities into basic actions. Neural Comput Applic 32(21):16387\u201316400","journal-title":"Neural Comput Applic"},{"issue":"4","key":"11438_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3448974","volume":"54","author":"VS Lalapura","year":"2021","unstructured":"Lalapura VS, Amudha J, Satheesh HS (2021) Recurrent neural networks for edge intelligence: A survey. ACM Comput Surv (CSUR) 54(4):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"13","key":"11438_CR17","doi-asserted-by":"publisher","first-page":"8519","DOI":"10.1007\/s11042-018-5843-6","volume":"79","author":"S Lingaswamy","year":"2020","unstructured":"Lingaswamy S, Kumar D (2020) An efficient moving object detection and tracking system based on fractional derivative. Multimed Tools Appl 79(13):8519\u20138537","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"11438_CR18","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu L et al (2020) Deep learning for generic object detection: A survey. Int J Comput Vis 128(2):261\u2013318","journal-title":"Int J Comput Vis"},{"issue":"2","key":"11438_CR19","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/computation9020024","volume":"9","author":"GA Mart\u00ednez-Mascorro","year":"2021","unstructured":"Mart\u00ednez-Mascorro GA et al (2021) Criminal intention detection at early stages of shoplifting cases by using 3D convolutional neural networks. Computation 9(2):24","journal-title":"Computation"},{"key":"11438_CR20","unstructured":"National Retail Federation (2018) National retail security survey"},{"key":"11438_CR21","doi-asserted-by":"crossref","unstructured":"Nguyen TN, LyNQ (2017) Abnormal activity detection based on dense spatial-temporal features and improved one-class learning. Proceedings of the Eighth International Symposium on Information and Communication Technology","DOI":"10.1145\/3155133.3155147"},{"key":"11438_CR22","doi-asserted-by":"crossref","unstructured":"Pienaar SW, Malekian R (2019) Human activity recognition using LSTM-RNN deep neural network architecture. 2019 IEEE 2nd Wireless Africa Conference (WAC). IEEE","DOI":"10.1109\/AFRICA.2019.8843403"},{"issue":"45","key":"11438_CR23","doi-asserted-by":"publisher","first-page":"34141","DOI":"10.1007\/s11042-020-09194-w","volume":"79","author":"HA Rashwan","year":"2020","unstructured":"Rashwan HA et al (2020) Action representation and recognition through temporal co-occurrence of flow fields and convolutional neural networks. Multimed Tools Appl 79(45):34141\u201334158","journal-title":"Multimed Tools Appl"},{"key":"11438_CR24","doi-asserted-by":"crossref","unstructured":"Singh DK, Kushwaha DS (2016) Tracking movements of humans in a real-time surveillance scene. In: M Pant, K Deep, JC Bansal, A Nagar, KN Das (eds) Proceedings of fifth international conference on soft computing for problem solving. Springer, Singapore","DOI":"10.1007\/978-981-10-0451-3_45"},{"key":"11438_CR25","volume-title":"International Conference on Advanced Informatics for Computing Research","author":"DK Singh","year":"2018","unstructured":"Singh DK (2018) Human Action Recognition in Video. In: Luhach Ashish Kumar, Singh Dharm, Hsiung Pao-Ann, Hawari Kamarul Bin Ghazali, Lingras Pawan, Singh Pradeep Kumar (eds) International Conference on Advanced Informatics for Computing Research. Springer, Singapore"},{"key":"11438_CR26","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.procs.2020.04.036","volume":"171","author":"DK Singh","year":"2020","unstructured":"Singh DK et al (2020) Human crowd detection for city wide surveillance. Procedia Comput Sci 171:350\u2013359","journal-title":"Procedia Comput Sci"},{"key":"11438_CR27","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.patcog.2017.01.001","volume":"65","author":"D Singh","year":"2017","unstructured":"Singh D, Mohan CK (2017) Graph formulation of video activities for abnormal activity recognition. Pattern Recognit 65:265\u2013272","journal-title":"Pattern Recognit"},{"issue":"1","key":"11438_CR28","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s00521-020-05018-y","volume":"33","author":"T Singh","year":"2021","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","journal-title":"Neural Comput Applic"},{"key":"11438_CR29","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00678"},{"issue":"1","key":"11438_CR30","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang H et al (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60\u201379","journal-title":"Int J Comput Vis"},{"key":"11438_CR31","doi-asserted-by":"publisher","first-page":"146533","DOI":"10.1109\/ACCESS.2019.2946000","volume":"7","author":"C Wang","year":"2019","unstructured":"Wang C et al (2019) Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access 7:146533\u2013146541","journal-title":"IEEE Access"},{"key":"11438_CR32","doi-asserted-by":"publisher","first-page":"56855","DOI":"10.1109\/ACCESS.2020.2982225","volume":"8","author":"K Xia","year":"2020","unstructured":"Xia K, Huang J, Wang H (2020) LSTM-CNN architecture for human activity recognition. IEEE Access 8:56855\u201356866","journal-title":"IEEE Access"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11438-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11438-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11438-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T07:31:55Z","timestamp":1655883115000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11438-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":32,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["11438"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11438-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,13]]},"assertion":[{"value":"31 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}