Detecting anomalous crowd scenes by oriented Tracklets’ approach in active contour region

Abstract

Video imagery based crowd analysis has become a topic of great interest for public safety at the venues of mass gathering events. This paper presents a novel approach to detect anomalous scene in high density crowded places. We propose an oriented tracklets approach in active contour region and measure the entropy and temporal occupancy deviation of oriented tracklets over the frames. To this end, an oriented trajectory algorithm is designed to extract tracklets of moving crowd. For trajectory extraction, spatio-temporal interest points are detected by adopting Harris corner features. The detected interest points are tracked over the frames within the optimized region. An active contour segmentation approach is applied to optimize the tracking region as a moving crowd is not distributed in entire frame region. The flow direction of each oriented tracklet is distributed into histogram bins at a specified interval, which defines the flow of collective motion pattern. A real-time scene updating procedure is also followed to adapt the changes of crowd scenes. Further, an entropy of histogram of oriented tracklets is computed based on the probability of occurrence of the tracklets. It has been shown that entropy of flow direction changes markedly in the unusual state of affairs. A simulation on a large number of the anomalous scene has been exercised to see the characteristics of an entropy. Also, temporal occupancy deviation is computed which measures the area occupied by the extracted tracklets of the crowd during a certain interval of time. If entropy and temporal occupancy deviation increase beyond a certain threshold, an alert is issued to detect anomaly to prevent potentially dangerous crowd-related disasters. Experiments conducted on three publicly available benchmark crowd datasets such as UMN, UCF Web, and Violent Flows, obtained interesting and promising results. We also evaluated some manually collected challenging real-world crowd video sequences. We compared the proposed approach with various state-of-the-art methods, and achieve remarkable accuracy while maintaining the lower computational complexity.