This paper introduces a method for abnormal event detection in video. The video is divided into a set of cubic patches. A new descriptor for representing the video patches is proposed. This descriptor is created based on the structure similarity between a patch and nine neighboring patches of it. All training normal patches in respect to the proposed descriptor are represented and then modeled using a Gaussian distribution as the reference model. In test phase, those patches which are not

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... to the reference model are labeled as anomaly. We have evaluated the proposed method on two UCSD 1 and UMN 2 popular standard benchmarks. The performance of the presented method is similar to state-of-the-art methods and also is very fast.