Novel video anomaly detection method based on global-local self-attention network - Jing YANG; Chengmao WU; Liuping ZHOU (Tongxin xuebao)
In order to improve the accuracy of video anomaly detection, a novel video anomaly detection method based on global-local self-attention network was proposed.Firstly, the video sequence and the corresponding RGB sequence were fused to highlight the motion change of the object.Secondly, the temporal correlation of the video sequence in the local area was captured by the expansion convolution layer, along with the self-attention network was utilized to compute the global temporal dependencies of the video sequence.Meanwhile, by deepening the basic network U-Net and combining the relevant motion and representation constraints, the network model was trained end-to-end to improve the detection accuracy and robustness of the model.Finally, experiments were carried out on the public data sets UCSD Ped2, CUHK Avenue and ShanghaiTech, as well as the test results were visually analyzed.The experimental results show that the detection accuracy AUC of the proposed method reaches 97.4%, 86.8% and 73.2% respectively, which is obviously better than that of the compared methods.