Please use this identifier to cite or link to this item:
Title: Abnormal event detection by a weakly supervised temporal attention network
Authors: Zheng, Xiangtao
Zhang, Yichao
Zheng, Yunpeng
Luo, Fulin
Lu, Xiaoqiang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Zheng, 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.
Journal: CAAI Transactions on Intelligence Technology 
Abstract: Abnormal 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.
ISSN: 2468-2322
DOI: 10.1049/cit2.12068
Schools: School of Electrical and Electronic Engineering 
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Citations 20

Updated on Feb 29, 2024

Web of ScienceTM
Citations 20

Updated on Oct 27, 2023

Page view(s)

Updated on Feb 29, 2024

Download(s) 50

Updated on Feb 29, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.