Two methods were proposed to solve the problem of incomplete multi-scale feature extraction of traditional video ano-maly detection methods in different scenes.One was a UNet3+based generative adversarial network detection method(U3P2)for simple scenes,and the other was a UNet++based adaptive generative adversarial network method(UP3)for complex scenes.Two ways were used to generate predictions for continuous input video frames.Predictions for continuous input video frames was generated,various loss functions and optical flow models were incorporated to learn their appearance and motion information.Performance was evaluated by calculating the area under the curve(AUC).The U3P2 method increases the AUC of Ped2 dataset by about 0.6％with 6.3 M parameters,while the UP3 method increases the AUC of Avenue dataset by about 0.8％.It is verified that it can cope with anomaly detection tasks in different scenes.
维普
万方数据