A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos

Highlights：

• We propose a cascaded model composed of a frame reconstruction network and an optical flow prediction network. By predicting optical flow based on reconstruction frame, the model increases the prediction error of optical flow containing abnormal events.

• We propose to choose a model for anomaly detection according to the reconstruction error of pseudo abnormal sample, which restricts the ability of test model to represent abnormal frames and achieves the tradeoff between the generalization of test model for normal and anomaly.

• We propose a two-step calculation function of abnormality score, which takes into account the spatial locality and temporal continuity of abnormal events.

• Experiments on three datasets demonstrate the competitive performance of our model compared with state-of-the-art methods.