{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:25:16Z","timestamp":1772767516880,"version":"3.50.1"},"reference-count":48,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61971007"],"award-info":[{"award-number":["61971007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61571013"],"award-info":[{"award-number":["61571013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:p> Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model\u2019s parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed. <\/jats:p>","DOI":"10.1142\/s0129065724500497","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T09:53:10Z","timestamp":1717149190000},"source":"Crossref","is-referenced-by-count":4,"title":["Abnormal Behavior Recognition Based on 3D Dense Connections"],"prefix":"10.1142","volume":"34","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Zhanhe","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Chaochao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]},{"given":"Yuanyao","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"S0129065724500497BIB001","first-page":"23","volume-title":"Int. Conf. Advances in Signal Processing and Artificial Intelligence","author":"Yu Z.","year":"2023"},{"key":"S0129065724500497BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.09.029"},{"key":"S0129065724500497BIB003","doi-asserted-by":"publisher","DOI":"10.3233\/ICA-230706"},{"key":"S0129065724500497BIB004","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12819"},{"key":"S0129065724500497BIB005","doi-asserted-by":"publisher","DOI":"10.3390\/ani13030413"},{"key":"S0129065724500497BIB006","doi-asserted-by":"publisher","DOI":"10.3390\/su15021041"},{"key":"S0129065724500497BIB007","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12949"},{"key":"S0129065724500497BIB008","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12494"},{"key":"S0129065724500497BIB009","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12647"},{"key":"S0129065724500497BIB010","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2020-0043"},{"key":"S0129065724500497BIB011","doi-asserted-by":"publisher","DOI":"10.1159\/000512985"},{"key":"S0129065724500497BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3190448"},{"key":"S0129065724500497BIB013","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-023-02032-0"},{"key":"S0129065724500497BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"S0129065724500497BIB015","first-page":"919","volume":"28","author":"Simonyan K.","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"S0129065724500497BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.590"},{"key":"S0129065724500497BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"S0129065724500497BIB018","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065714300101"},{"key":"S0129065724500497BIB019","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330830"},{"key":"S0129065724500497BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3125040"},{"key":"S0129065724500497BIB021","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070801"},{"key":"S0129065724500497BIB022","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2519102"},{"key":"S0129065724500497BIB023","doi-asserted-by":"publisher","DOI":"10.1145\/2523819"},{"key":"S0129065724500497BIB024","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065719500278"},{"issue":"6","key":"S0129065724500497BIB025","first-page":"409","volume":"222","author":"Mallapragada G.","year":"2008","journal-title":"Proc. Inst. Mech. Eng."},{"key":"S0129065724500497BIB026","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2016.06.007"},{"key":"S0129065724500497BIB027","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12695"},{"key":"S0129065724500497BIB028","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065722500344"},{"key":"S0129065724500497BIB029","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2670780"},{"key":"S0129065724500497BIB030","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.617"},{"key":"S0129065724500497BIB031","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500343"},{"key":"S0129065724500497BIB032","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"S0129065724500497BIB033","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"S0129065724500497BIB034","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.11.067"},{"key":"S0129065724500497BIB035","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10618-3"},{"key":"S0129065724500497BIB036","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103518"},{"key":"S0129065724500497BIB037","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"S0129065724500497BIB038","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.11.005"},{"key":"S0129065724500497BIB039","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-013-0189-8"},{"key":"S0129065724500497BIB040","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23117-4_21"},{"key":"S0129065724500497BIB041","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2020.105628"},{"key":"S0129065724500497BIB042","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2020.105520"},{"issue":"5","key":"S0129065724500497BIB043","first-page":"1","volume":"34","author":"Bengio Y.","year":"2007","journal-title":"Large-scale Kernel Mach."},{"key":"S0129065724500497BIB044","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00685"},{"key":"S0129065724500497BIB045","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"issue":"1","key":"S0129065724500497BIB046","first-page":"857","volume":"35","author":"Liu X.","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"S0129065724500497BIB047","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2682102"},{"key":"S0129065724500497BIB048","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.12.022"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065724500497","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T06:33:37Z","timestamp":1721111617000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0129065724500497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,16]]},"references-count":48,"journal-issue":{"issue":"09","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["10.1142\/S0129065724500497"],"URL":"https:\/\/doi.org\/10.1142\/s0129065724500497","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,16]]},"article-number":"2450049"}}