3D U-Net for Video Anomaly Detection
Authors: 
JingXian Yang, YiHeng Cai, Dan Liu, Jin XieAuthors Info & Claims
JingXian Yang, YiHeng Cai, Dan Liu, Jin XieAuthors Info & ClaimsPages 1640 - 1645
Abstract
With the widespread of surveillance video application, anomaly detection in surveillance video is significant for safety maintenance. In this paper, we propose a new abnormal detection framework, which contains an improved future frame prediction model based on GAN network to locate the abnormal events in the video. Unlike the previous U-Net network which is usually used as prediction model in abnormal event detection, 3D U-Net is adopted in our proposed network for predicting future frames which can fuse the spatial and temporal features simultaneously by using 3-dimensional convolution. For normal events, the improved prediction network takes into account the temporal features, resulting in low abnormal scores, while abnormal events have high abnormal scores. Experiments show that this method can get good results on the Avenue, UCSD Ped2 and ShanghaiTech datasets, and the AUC values on these datasets could reach 86.0%, 96.3% and 73.6% respectively.
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Index Terms
- 3D U-Net for Video Anomaly Detection
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Published In
October 2021
1723 pages
ISBN:9781450384322
DOI:10.1145/3501409
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Published: 31 December 2021
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- National Key Research and Development Program of China under Grant
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EITCE 2021
EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering
October 22 - 24, 2021
Xiamen, China
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EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
Overall Acceptance Rate 508 of 972 submissions, 52%
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