Clustering Driven Deep Autoencoder for Video Anomaly Detection

Because of the ambiguous definition of anomaly and the complexity of real data, video anomaly detection is one of the most challenging problems in intelligent video surveillance. Since the abnormal events are usually different from normal events in appearance and/or in motion behavior, we address this issue by designing a novel convolution autoencoder architecture to separately capture spatial and temporal informative representation. The spatial part reconstructs the last individual frame (LIF), while the temporal part takes consecutive frames as input and RGB difference as output to simulate the generation of optical flow. The abnormal events which are irregular in appearance or in motion behavior lead to a large reconstruction error. Besides, we design a deep k-means cluster to force the appearance and the motion encoder to extract common factors of variation within the dataset. Experiments on some publicly available datasets demonstrate the effectiveness of our method with the state-of-the-art performance.