Anomalous activity classification in the distributed camera network

Unlike existing methods that used the human actions or trajectories to analyze the human activity in overlapping field-of-views, this paper proposes the appearance and travel time-based human activity classification in the camera network of non-overlapping field-of-views. The mixture of Gaussian-based appearance similarity model incorporates the appearance variance between different cameras to address changes in varying lighting conditions. To address the problem of limited labeled training data, we propose the use of semi-supervised expectation-maximization algorithm for activity classification. The human activities observed in a simulated camera network with nine cameras and twenty-five nodes are classified into one normal and three anomalous classes. A similar camera network is built and tested in real-life experiments, in which the proposed approach achieves satisfactory performance.