Abnormal event detection by a weakly supervised temporal attention network

Xiangtao Zheng, Yichao Zhang, Yunpeng Zheng, Fulin Luo, Xiaoqiang Lu
2021 CAAI Transactions on Intelligence Technology  
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
more » ... tions 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. K E Y W O R D S human detection, video analysis | INTRODUCTION Abnormal event detection has attracted extraordinary attention in the computer vision community [1] due to its critical applications such as video surveillance [2], human computer interfaces [3], violence alerting [4], evidence investigation [5] etc. Generally speaking, abnormal events mean patterns or motions that rarely occur in videos and are different from existing events. The core objective of abnormal event detection is to automatically identify abnormal events from surveillance videos [6] . However, the low quality of videos, shadows, 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.
doi:10.1049/cit2.12068 fatcat:tv72n5lzyzezdi3emajgwiv7ja