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Detection of global abnormal events in crowded scenes

In this paper, we propose a novel method for global abnormal events detection in crowded scenes. Each video is described as the set of overlapping space-time cubes. The histogram of optical flow orientation and motion magnitude are used as global feature descriptor to capture the motion magnitude and orientation of the normal and abnormal events. The motion-rich space-time cubes are selected to enhance the computational speed of the algorithm and omit background subtraction step, forms the proposed approach novel and unique. Simple and cost-effective one-class SVM classifier is used to distinguish between normal and abnormal events in a video. The one-class SVM classifier is trained with normal events from the training data. The proposed method is tested on benchmark UMN and PETS2009 dataset. The improved results are reported to evaluate the performance of the proposed method.