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 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.
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Poppe, C., Martens, G., Lambert, P., Van de Walle, R. (2007). Mixture Models Based Background Subtraction for Video Surveillance Applications. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_4
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DOI: https://doi.org/10.1007/978-3-540-74272-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
Online ISBN: 978-3-540-74272-2
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