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dc.contributor.authorZheng, Xiangtaoen_US
dc.contributor.authorZhang, Yichaoen_US
dc.contributor.authorZheng, Yunpengen_US
dc.contributor.authorLuo, Fulinen_US
dc.contributor.authorLu, Xiaoqiangen_US
dc.identifier.citationZheng, X., Zhang, Y., Zheng, Y., Luo, F. & Lu, X. (2022). Abnormal event detection by a weakly supervised temporal attention network. CAAI Transactions On Intelligence Technology, 7(3), 419-431.
dc.description.abstractAbnormal event detection aims to automatically identify unusual events that do not comply with expectation. Recently, many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds. However, the specific categories of abnormal events are mostly neglect, which are important to help in monitoring agents to make decisions. In this study, a Temporal Attention Network (TANet) is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner. The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels. An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value. Finally, to learn anomaly scores and specific categories, three constraints are considered: event category constraint, event separation constraint and temporal smoothness constraint. Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.en_US
dc.relation.ispartofCAAI Transactions on Intelligence Technologyen_US
dc.rights© 2021 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleAbnormal event detection by a weakly supervised temporal attention networken_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsHuman Detectionen_US
dc.subject.keywordsVideo Analysisen_US
dc.description.acknowledgementThis work was supported in part by the National Science Fund for Distinguished Young Scholars under grant no. 61925112, in part by the National Natural Science Foundation of China under grant no. 61806193 and grant no. 61772510, in part by the Innovation Capability Support Program of Shaanxi under grant no. 2020KJXX‐091, and in part by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences under grant no. QYZDY‐SSW‐JSC044.en_US
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