Behavior recognition and anomaly behavior detection using clustering
Asghar Feizi, Ali Aghagolzadeh, Hadi Seyedarabi · 2012
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.