Video Anomaly Detection Based on Implicit View Transformation
TAN Ming-pi LENG Jia-xu · DOAJ (DOAJ: Directory of Open Access Journals) · 2022
Existing deep learning-based video anomaly detection methods all detect anomalies in video clips under a single view,ignoring the importance of view information in video anomaly detection.Under a single view,when anomalies are occluded or not obvious,the performance of existing algorithms will suffer drops.To avoid this problem,the author firstly introduces the concept of view transformation into video anomaly detection,which improves the robustness of the model by judging abnormalities from multiple views.However,due to the lack of multi-view supervision information in the dataset,it is difficult to achieve explicit view transformation.Specifically,in order to reflect the idea of view transformation,the author proposes a video anomaly detection method based on implicit view transformation,using the optical flow information between frames to warp the implicit view information of the previous frame to the target frame,so as to realize the implicit view transformation from the target frame to the previous frame.And then,the method performs secondary anomaly detection on the target frame after view transformation.Experimental results show that the proposed method responds more sensitively to abnormal data and has a more robust normal data fitting ability.The AUC values on the UCSD Ped2 and CUHK Avenue datasets reached 97.0% and 88.9%,respectively.