Automatic behavior model selection by iterative learning and abnormality recognition

Automatic behavior recognition is one important task of community security and surveillance system. In this paper, a novel method is proposed for automatic selection of behavior models by iterative learning and abnormality recognition. The method is mainly composed of the following two steps: (1) The models of normal behaviors are automatically selected and trained by combining Dynamic Time Warping based spectral clustering and iterative learning; (2) Maximum A Posteriori adaptation technique is used to estimate the parameters of abnormal behavior models from those of normal behavior models. Compared with the related works in the literature, our method has three advantages: (1) automatic selection of the class number of normal behaviors from large unlabeled video data according to the process of iterative learning, (2) semi-supervised learning of abnormal behavior models, and (3) avoidance of the running risk of over-fitting during learning the Hidden Markov Models of behaviors in case of sparse data. Experiments demonstrate the effectiveness of our proposed method.