Detection and localization of crowd behavior using a novel tracklet-based model

Abstract

In this paper, two novel descriptors are introduced to detect and localize abnormal behaviors in crowded scenes. The first proposed descriptor is based on the orientation and magnitude of short trajectories extracted by tracking interest points in spatio-temporal 3D patches. The proposed descriptor employs a novel simplified Histogram of Oriented Tracklets (sHOT), which is shown to be very effective in the task of crowd abnormal behavior detection. In this scheme, abnormal behaviors are detected at different levels, namely spatio-temporal level and frame level. By combining the first proposed descriptor and the dense optical flow model, we propose our second framework which is able to localize the abnormal behavior areas in video sequences. The evaluation of our simple but yet effective descriptors on different state-of-the-art datasets, namely UCSD, UMN and Violence in Crowds yields very promising results in abnormality detection and outperforming different former state-of-the-art descriptors.