In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The\udproposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and\uddirection invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample\uddeviates significantly from the normal behavior, it is detected as abnormal crowd behavior. Although there are\udmany optical flow based features for crowd behaviour analysis, this is the first time the angle difference between optical flow vectors in the current frame and in the previous frame is considered as a anomaly feature.\udEvaluations on UMN and PETS2009 datasets show that the proposed method performs competitive results compared to the state-of-the-art methods.
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