{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:41:14Z","timestamp":1780512074380,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"crossref","award":["No. 2021C03151"],"award-info":[{"award-number":["No. 2021C03151"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["No.Y20F020113"],"award-info":[{"award-number":["No.Y20F020113"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16150-x","type":"journal-article","created":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T11:02:21Z","timestamp":1690023741000},"page":"15733-15750","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Behavior detection and evaluation based on multi-frame MobileNet"],"prefix":"10.1007","volume":"83","author":[{"given":"Linqi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1773-9760","authenticated-orcid":false,"given":"Xiuhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifu","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuesheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"key":"16150_CR1","doi-asserted-by":"crossref","unstructured":"Akpinar KN, Genc S, Karagol S (2020) Chest x-ray abnormality detection based on squeezenet. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp 1\u20135","DOI":"10.1109\/ICECCE49384.2020.9179404"},{"key":"16150_CR2","unstructured":"An J, Cheng Y, He X, Gui X, Wu S, Zhang X (2021) Multiuser behavior recognition module based on dc-dmn. IEEE Sens J, pp 1\u20131"},{"key":"16150_CR3","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4724\u20134733","DOI":"10.1109\/CVPR.2017.502"},{"key":"16150_CR4","doi-asserted-by":"crossref","unstructured":"Chen Y, Ge H, Liu Y, Cai X, Sun L (2023) Agpn: Action granularity pyramid network for video action recognition. IEEE Trans Circ Syst Video Technol, pp 1\u20131","DOI":"10.1109\/TCSVT.2023.3235522"},{"key":"16150_CR5","doi-asserted-by":"crossref","unstructured":"Du W, Wang Y, Qiao Y (2017) Rpan: An end-to-end recurrent pose-attention network for action recognition in videos. In 2017 IEEE International Conference on Computer Vision (ICCV), pp 3745\u20133754","DOI":"10.1109\/ICCV.2017.402"},{"key":"16150_CR6","doi-asserted-by":"crossref","unstructured":"Du B, Zhao J, Cao M, Li M, Yu H (2021) Behavior recognition based on improved faster rcnn. In 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp 1\u20136","DOI":"10.1109\/CISP-BMEI53629.2021.9624427"},{"key":"16150_CR7","doi-asserted-by":"crossref","unstructured":"Gomes R, Rozario P, Adhikari N (2021) Deep learning optimization in remote sensing image segmentation using dilated convolutions and shufflenet. In 2021 IEEE International Conference on Electro Information Technology (EIT), pp 244\u2013249","DOI":"10.1109\/EIT51626.2021.9491910"},{"key":"16150_CR8","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press"},{"key":"16150_CR9","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"key":"16150_CR10","unstructured":"Howard A, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. 04"},{"key":"16150_CR11","doi-asserted-by":"crossref","unstructured":"Hu K, Jin J, Zheng F, Weng L, Ding Y (2022) Overview of behavior recognition based on deep learning. Artif Intell Rev","DOI":"10.1007\/s10462-022-10210-8"},{"issue":"4","key":"16150_CR12","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1109\/TPAMI.2016.2599174","volume":"39","author":"D Jeff","year":"2017","unstructured":"Jeff D, Anne HL, Marcus R, Subhashini V, Sergio G, Kate S, Trevor D (2017) Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans Pattern Anal Mach Intell 39(4):677\u2013691","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"16150_CR13","first-page":"04","volume":"61","author":"S Juergen","year":"2014","unstructured":"Juergen S (2014) Deep learning in neural networks: An overview. Neural Netw 61:04","journal-title":"Neural Netw"},{"key":"16150_CR14","doi-asserted-by":"crossref","unstructured":"Kacem A, Daoudi M, Amor BB, Berretti S, Paiva J (2018) A novel geometric framework on gram matrix trajectories for human behavior understanding. IEEE Trans Pattern Anal Mach Intell, PP:1\u20131, 09","DOI":"10.1109\/TPAMI.2018.2872564"},{"key":"16150_CR15","first-page":"06","volume":"1","author":"S Karen","year":"2014","unstructured":"Karen S, Andrew Z (2014) Two-stream convolutional networks for action recognition in videos. Advan Neural Inform Process Syst 1:06","journal-title":"Advan Neural Inform Process Syst"},{"issue":"2","key":"16150_CR16","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TCSVT.2019.2893318","volume":"30","author":"Longteng Kong","year":"2020","unstructured":"Kong Longteng, Huang Di, Qin Jie, Wang Yunhong (2020) A joint framework for athlete tracking and action recognition in sports videos. IEEE Trans Circ Syst Video Technol 30(2):532\u2013548","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"16150_CR17","doi-asserted-by":"crossref","unstructured":"Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) Hmdb51: A large video database for human motion recognition. pp 2556\u20132563, 11","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"16150_CR18","doi-asserted-by":"crossref","unstructured":"Kumar D, Priyanka T, Murugesh A, Kafle VP (2020) Visual action recognition using deep learning in video surveillance systems. In 2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K), pp 1\u20138","DOI":"10.23919\/ITUK50268.2020.9303222"},{"key":"16150_CR19","doi-asserted-by":"crossref","unstructured":"Li H, Huang J, Zhou M, Shi Q, Fei Q (2022) Self-attention pooling-based long-term temporal network for action recognition. IEEE Trans Cognitive Develop Syst, pp 1\u20131","DOI":"10.1109\/TCDS.2022.3145839"},{"key":"16150_CR20","unstructured":"Limin W, Yuanjun X, Yu ZW, Lin QD, Xiaoou T, Luc VG (2016) Temporal segment networks: Towards good practices for deep action recognition. 9912:10"},{"key":"16150_CR21","doi-asserted-by":"crossref","unstructured":"Liu W, Li H, Zhang H (2022) Dangerous driving behavior recognition based on hand trajectory. Sustainability, 14(19)","DOI":"10.3390\/su141912355"},{"key":"16150_CR22","unstructured":"Ng JY-H, Hausknecht M, Vijayanarasimhan S, Vinyals O, Monga R, Toderici G (2015) Beyond short snippets: Deep networks for video classification. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4694\u20134702"},{"key":"16150_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen C, Nguyen N, Huynh S, Nguyen V, Nguyen S (2022) Learning generalized feature for temporal action detection: Application for natural driving action recognition challenge. In 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 3248\u20133255","DOI":"10.1109\/CVPRW56347.2022.00367"},{"key":"16150_CR24","doi-asserted-by":"crossref","unstructured":"Qiu Z, Yao T, Mei T (2017) Learning spatio-temporal representation with pseudo-3d residual networks. In 2017 IEEE International Conference on Computer Vision (ICCV), pp 5534\u20135542","DOI":"10.1109\/ICCV.2017.590"},{"key":"16150_CR25","doi-asserted-by":"crossref","unstructured":"Rabano SL, Cabatuan MK, Sybingco E, Dadios EP, Calilung EJ (2018) Common garbage classification using mobilenet. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), pp 1\u20134","DOI":"10.1109\/HNICEM.2018.8666300"},{"key":"16150_CR26","doi-asserted-by":"crossref","unstructured":"Rahadian R, Suyanto S (2019) Deep residual neural network for age classification with face image. In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp 21\u201324","DOI":"10.1109\/ISRITI48646.2019.9034664"},{"key":"16150_CR27","doi-asserted-by":"crossref","unstructured":"Rismiyati, Endah SN, Khadijah, Shiddiq IN (2020) Xception architecture transfer learning for garbage classification. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), pp 1\u20134","DOI":"10.1109\/ICICoS51170.2020.9299017"},{"key":"16150_CR28","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. pp 4510\u20134520, 06","DOI":"10.1109\/CVPR.2018.00474"},{"key":"16150_CR29","doi-asserted-by":"crossref","unstructured":"Silva MO, Valad\u00e3o MDM, Cavalcante VLG, Santos AV, Torres GM, Mattos EVCU, Pereira AMC, Uch\u00f4a MS, Torres LM, Linhares JEBS, Silva NEM, Silva AP, Cruz CFS, R\u00f4mulo SF, Belem RJS, Bezerra TB, Waldir SS, Carvalho CB (2022) Action recognition of industrial workers using detectron2 and automl algorithms. In 2022 IEEE International Conference on Consumer Electronics - Taiwan, pp 321\u2013322","DOI":"10.1109\/ICCE-Taiwan55306.2022.9869197"},{"issue":"102725","key":"16150_CR30","first-page":"12","volume":"66","author":"J Singh","year":"2019","unstructured":"Singh J, Goyal G (2019) Identifying biometrics in the wild- a time, erosion and neural inspired framework for gait identification. J Visual Commun Image Representation 66(102725):12","journal-title":"J Visual Commun Image Representation"},{"key":"16150_CR31","unstructured":"Soomro K, Zamir A, Shah M (2012) Ucf101: A dataset of 101 human actions classes from videos in the wild. CoRR, 12"},{"key":"16150_CR32","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. 4489\u20134497, 12","DOI":"10.1109\/ICCV.2015.510"},{"issue":"1","key":"16150_CR33","doi-asserted-by":"publisher","first-page":"1950027","DOI":"10.1142\/S0129065719500278","volume":"30","author":"X Wang","year":"2020","unstructured":"Wang X, Yan WQ (2020) Human gait recognition based on frame-by-frame gait energy images and convolutional long short term memory. Int J Neural Syst 30(1):1950027","journal-title":"Int J Neural Syst"},{"key":"16150_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"16150_CR35","doi-asserted-by":"crossref","unstructured":"Wang X, Yan WQ (2022) Human identification based on gait manifold. Appl Intell,","DOI":"10.1007\/s10489-022-03818-4"},{"issue":"10","key":"16150_CR36","doi-asserted-by":"publisher","first-page":"4941","DOI":"10.1109\/TIP.2019.2917283","volume":"28","author":"B Xu","year":"2019","unstructured":"Xu B, Hao Y, Yingbin Z, Heng W, Tianyu L, Yu-Gang J (2019) Dense dilated network for video action recognition. IEEE Trans Image Process 28(10):4941\u20134953","journal-title":"IEEE Trans Image Process"},{"key":"16150_CR37","unstructured":"Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. 4, pp 441\u2013444, 01"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16150-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16150-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16150-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:01:28Z","timestamp":1706691688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16150-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,22]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["16150"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16150-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,22]]},"assertion":[{"value":"20 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 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":"The authors declare that there is no conflict of interests regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}