Abstract
Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed. This paper proposes an update of the popular Mixture of Gaussian Models technique. Experimental analysis shows a lack of this technique to cope with quick illumination changes. A different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. Finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.
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Poppe, C., Martens, G., Lambert, P., Van de Walle, R. (2007). Improved Background Mixture Models for Video Surveillance Applications. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_23
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DOI: https://doi.org/10.1007/978-3-540-76386-4_23
Publisher Name: Springer, Berlin, Heidelberg
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