Research on Crowd Video Anomaly Detection Algorithm Based on Dual-branch

CAI Yiheng; LIU Tianhao; LIU Jiaqi; GUO Yajun; HU Shaobin

doi:10.11999/JEIT210341

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2496-2503

CAI Yiheng, LIU Tianhao, LIU Jiaqi, GUO Yajun, HU Shaobin. Research on Crowd Video Anomaly Detection Algorithm Based on Dual-branch[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2496-2503. doi: 10.11999/JEIT210341

Citation:

CAI Yiheng, LIU Tianhao, LIU Jiaqi, GUO Yajun, HU Shaobin. Research on Crowd Video Anomaly Detection Algorithm Based on Dual-branch[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2496-2503. doi: 10.11999/JEIT210341

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Research on Crowd Video Anomaly Detection Algorithm Based on Dual-branch

doi: 10.11999/JEIT210341 cstr: 32379.14.JEIT210341

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Funds: The National Key Research and Development Program of China(2017YFC1703302)

Received Date: 2021-04-23
Rev Recd Date: 2021-09-15

Available Online: 2021-09-29

Publish Date: 2022-07-25

Abstract

Abstract

This paper studies the task of crowd anomaly detection. Considering the problems of crowd scene video background being redundant, susceptible to light and noise, and actual deployment, a Crowd anomaly Multi-scale feature memory network (CaMsm-net) is proposed. In order to distinguish the occurrence of anomalies from multiple angles and integrate better various types of information, a dual-branch shared unit structure is adopted by the network, the original frame and the background modeled frame are simultaneously input into the network structure. To predict the two branches separately, the prediction error was used to determine the abnormality, and from the perspective of practical application, the depthwise separable convolutionand data augmentation methods are added to the framework to ensure the accuracy of detection and the feasibility of deployment. Experiments on the public University of MinNesota (UMN) population dataset and the actual monitoring of the train station exit dataset show that the Area Under Curve (AUC) indicators reach 99.2% and 84.1% respectively, and the average detection accuracy rates are 95.9% and 81.7%, which proves the proposed algorithm can better detect the occurrence of various crowd abnormalities and has wider applicability.
- Crowd anomaly detection,
- Dual-branch sharing,
- Background modeling,
- Space-time fusion

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References(16)

References

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