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 in the literature. This paper proposes a novel background subtraction technique based on the popular Mixture of Gaussian Models technique. Moreover edge-based image segmentation is used to improve the results of the proposed technique. Experimental analysis shows that our system outperforms the standard system both in processing speed and detection accuracy. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Poppe, C., Martens, G., Lambert, P., & Van De Walle, R. (2007). Mixture models based background subtraction for video surveillance applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4673 LNCS, pp. 28–35). Springer Verlag. https://doi.org/10.1007/978-3-540-74272-2_4
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