In order to improve the detection accuracy of abnormal group activity and reduce the impact of noise in anomaly detection

an algorithm for the detection of abnormal group activity by combining complex wavelet domain based denoising with particle swarm optimization twin support vector machine（ PSO-TSVM） is proposed. The Horn-Schunck optical flow method is used to extract the features of velocity

acceleration

direction of group activity and the feature of crowd density in the video. Non-subsampled dual-tree complex wavelet packet transform and bivariate model are used to remove the noises of the group activity features extracted from the video. Abnormal group activity in the video is detected by PSO-TSVM which is trained and tested using these denoised group activity features. Experimental results on UMN video dataset and self-built dataset show that the proposed algorithm has higher detection accuracy than the social force model and particle entropy model.