Sparse Representation and Weighted Clustering Based Abnormal Behavior Detection

Abnormal activity detection is a challenging problem divided into global abnormal activity detection and local abnormal activity detection. First, the hybrid histogram of optical flow feature is extracted; then, the double sparse representation is proposed to tackle the issue of global abnormal activity detection; finally, for the issue of local abnormal activity detection, the foreground of region of interest within the current frame is first detected, and then the method of online weighted clustering is utilized to detect local abnormal activity. Experiments results conducted on UMN datasets and UCSD datasets validate the advantages of the proposed method.