Visual data mining for crowd anomaly detection using artificial bacteria colony

This paper presents a novel method for global anomaly detection in crowded scenes. The optical flow of frames is used to extract the foreground of areas with people motions in the crowd in the form of layers. The optical flow between two frames generates one layer. The proposed method applies the metaheuristic of artificial bacteria colony as a robust algorithm to optimize the extracted layers. Artificial bacteria cover all regions of interest that have high movement between frames. The artificial bacteria colony adapts quickly to the most varied scenarios. Moreover, the algorithm has low sensibility to noise and to sudden changes in video lighting as captured by optical flow. The bacteria population of the colonies, its food storage and the colony’s centroid position regarding each optical flow layer, are used as input to train a Kohonen’s neural network. Once trained the network is able to detect specific events based on behavior patterns similarity, as produced by the bacteria colony during such events. Experiments are conducted on available public dataset. The achieved results show that the proposed method captures the dynamics of the crowd behavior successfully, revealing that the proposed scheme outperforms the available state-of-the-art algorithms for global anomaly detection.