SWARM-BASED MOTION FEATURES FOR ANOMALY DETECTION IN CROWDS

In this work we propose a novel approach to the detection of anomalous events occurring in crowded scenes. Swarm theory is applied for the creation of a motion feature first introduced in this work, the Histograms of Oriented Swarm Accelerations (HOSA), which are shown to effectively capture a scene's motion dynamics. The HOSA, together with the well known Histograms of Oriented Gradients (HOGs) describing appearance, are combined to provide a final descriptor based on both motion and appearance, to effectively characterize a crowded scene. Appearance and motion features are only extracted within spatiotemporal volumes of moving pixels (regions of interest) to ensure robustness to local noise and allow the detection of anomalies occurring only in a small region of the frame. Experiments and comparisons with the State of the Art (SoA) on a variety of benchmark datasets demonstrate the effectiveness of the proposed method, its flexibility and applicability to different crowd environments, and its superiority over currently existing approaches.