Abstract
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 crowd. 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 layers from optical flow. The artificial bacteria colony has the ability to adapt quickly to the most varied scenarios, extracting just relevant information from regions of interest. Moreover, the algorithm has low sensibility to noise and to sudden changes in video lighting as captured by optical flow. The bacteria population of 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 publicly available 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.
J. Ramos—The work of this author is supported by CAPES, the Coordination of Improvement of Higher Education Personnel of the Brazilian Federal Government.
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Ramos, J., Nedjah, N., Mourelle, L.M. (2017). Crowd Anomaly Detection Based on Optical Flow, Artificial Bacteria Colony and Kohonen’s Neural Network. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_23
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DOI: https://doi.org/10.1007/978-3-319-62395-5_23
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