Using optical flow and spectral clustering for behavior recognition and detection of anomalous behaviors

In this paper we propose an efficient method for behavior recognition and identification of anomalous behavior in video surveillance data. This approach consists of two phases of training and testing. In the training phase, first, we use background subtraction method to extract the moving pixels. Then optical flow vectors are extracted for moving pixels. We propose behavior features of each pixel as the average all optical flow vectors in the pixel over several frames in video data. Next, we use spectral clustering to classify behaviors wherein pixels that have similar behavior features are clustered together. Then we obtain a behavior model for each cluster using the normal distribution of the samples. Once the behavior models are obtained, in the testing phase, we use these models to detect anomalous behavior in a test video of the same scene. Experimental results on video surveillance sequences show the effectiveness and speed of proposed method.