Abnormal event detection in computer vision addresses the task of identifying events that deviate from expected behavior in video scenes. Issues, such as occlusion in crowded scenes, the powerful generalization capabilities of deep neural networks, and the heavy reliance on contextual information, make this task particularly challenging. To address these issues, we propose a cascaded form of abnormal detection framework that combines the paradigms of reconstruction and prediction in this paper. First, stochastic masking techniques are employed for image reconstruction to alleviate the overgeneralization of neural networks under abnormal conditions. Second, an innovative motion characterization of frame-difference streak streams is introduced to better characterize the motion of video frames in crowded scenes. Finally, a dual-channel autoencoder-based prediction network is introduced to jointly learn appearance and motion features. This network captures contextual information to better generate predictive features. Meanwhile, adversarial learning is introduced for abnormal inference to improve the detection performance. Experimental results on several benchmark datasets validate the effectiveness of our approach.
The application of abnormal event detection in video surveillance is an active research field, but due to the imbalance of positive and negative samples in surveillance video, abnormal event detection is full of challenges. In this paper, we propose a new abnormal event detection method based on appearance repair and motion consistency for detecting anomalous events. Specifically, the input image is partially masked and then fed into our proposed appearance repair autoencoder for image reconstruction, and then the motion consistency of images is constructed by our proposed optical flow network. The experimental results on the UCSD, CUHK Avenue datasets show the superiority of the detection performance of our method.
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