Abnormal activity detection using spatio-temporal feature and Laplacian sparse representation

Abstract

Abnormal activity detection in a video is a challenging and attractive task. In this paper, an approach using spatio-temporal feature and Laplacian sparse representation is proposed to tackle this problem. To detect the abnormal activity, we first detect interest points of a query video in the spatio-temporal domain. Then normalized combinational vectors, named HNF, are computed around the detected space-time inter- est points to characterize the video. After that, we utilize the Laplacian sparse representation framework and maximum pooling method to gain a more discriminative feature vector from the HNF set. Finally, the sup- port vector machine (SVM) is adopted to classify the feature vector as normal or abnormal. Experiments on two datasets demonstrate the sat- isfactory performance of the proposed approach.