JingXian Yang
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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