Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection

Abstract

As the uncertainty of abnormal events,the method of future frame prediction is chosen to detect abnormal events in video.The prediction model is trained with normal samples,so that the model can accurately predict the future frames without abnormal events.However,it cannot predict video frames with unknown events.Combining with apparent constraints and motion constraints,generative adversarial network is used to train the generator model for prediction.In order to reduce the loss of relative target features,a nonlocal attention Unet generator (NA-UnetG) model is proposed to improve the prediction accuracy of generator and the accuracy of abnormal video event detection.Experiments on datasets CUHK Avenue and UCSD Ped2 validate the effectiveness of the proposed method.The results show that the AUC of the proposed method is better than that of other methods,reaches 83.4% and 96.3%,respectively.

Keywords

• video anomaly event detection|generative adversarial network|video prediction|non-local attention mechanism|deep learning