Anomaly Detection In Crowded Scenes Using Dense Trajectories

Abnormal crowd behavior has become a popular research topic in recent years. This is related to a rise in the need for electronic video surveillance. Many methods have been proposed to detect abnormalities, but these methods rely on optical flow or classical classification techniques. We propose to follow the general pipeline used by previous works, but upgrade several components with state-of-theart techniques. Specifically, we use dense trajectories in place of optical flow, robust features such as social force, HOG, HOF, and MBH, and a single-class support vector machine. We achieve significant improvements in abnormality detection when compared with prior works.