Behavior recognition and anomaly behavior detection using clustering

In this paper we propose an approach for behavior modeling and detection of certain types of anomalous behavior. This approach consists of three basic parts. First, we propose busy-idle rates, as the behavior features, to define a behavior model for a block of pixels. Second, given a training set of normal data only, we propose spectral clustering for classifying behaviors wherein block of pixels that exhibit similar behavior models are clustered together. Then a behavior model for each cluster is obtained using the histogram of the samples. Once the behavior models are obtained, we use these models to perform anomalous behavior detection in a test video of the same scene. Experimental results on video surveillance sequences show the effectiveness and speed of proposed method.