Abstract
The paper presents a simple but effective method to detect abnormal crowd behaviors from real crowd videos. The method does not depend on person detection or segmentation, instead it takes the advantages of the use of cluster analysis such as robustness against variable numbers of people in the scenes. It lies in the use of the Bhattacharyya distance to measure differences in properties of clusters over time between frames. The normalized Bhattacharyya distance measure provides the knowledge of the state of abnormality. Experiments have been conducted on different real crowd videos covering both normal and abnormal activities. The experimental results show that distances between clusters of tracked corners on movers are reasonable ways to characterize abnormal behavior as the distances vary significantly in case of abnormalities.
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Sharif, M.H., Uyaver, S., Djeraba, C. (2010). Crowd Behavior Surveillance Using Bhattacharyya Distance Metric. In: Barneva, R.P., Brimkov, V.E., Hauptman, H.A., Natal Jorge, R.M., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Represented in Images. CompIMAGE 2010. Lecture Notes in Computer Science, vol 6026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12712-0_28
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DOI: https://doi.org/10.1007/978-3-642-12712-0_28
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