Global abnormal events detection in surveillance video — A hierarchical approach

In this paper, global abnormal events detection in the crowded scene is proposed. The proposed algorithm is based on a hierarchical approach. The method uses the histogram of optical flow orientation as a feature descriptor along with the magnitude of the optical flow to capture the motion. The nonlinear one-class support vector machine (SVM) classification algorithm is used to learn the normal events from the training data. After learning, one-class SVM detects abnormal events in the frame. The algorithm is fast with improved accuracy since it omits the background subtraction step and adopts hierarchy. The proposed method is tested on benchmark UMN unusual crowd activity dataset which contain global abnormal events. Promising results are reported which evaluates the performance of the proposed method.