Video anomaly detection guided by clustering learning

With the fuzzy boundary between normal and abnormal video data, which cannot be well distinguished by most methods, anomaly detection in video requires better characterization of the data. First we give a convolution-enhanced self-attentive video auto-encoder based on the U-Net architecture, which can extract richer image features. Secondly we design a dual-scale feature clustering structure for this encoder, which simultaneously compresses the channel and spatial structure features of the image to represent the features to obtain good coding characteristics and expand the boundary between normal and abnormal data. We also verify that our approach is equivalent to a class of auto-encoders for memory-guided learning. Finally, in the reconstruction task, since video auto-encoders are capable of triggering temporal time leakage phenomena that can lead to network performance degradation, we propose an anomaly score computation paradigm for video auto-encoders that utilizes the average frame anomaly score of a video clip to compute the first frame anomaly score in that video clip. Extensive experiments on three benchmark datasets show that our method outperforms most existing methods on large datasets with complex patterns. The code will be published at the following link: Anomaly-detection-guided-by-clustering-learning