Abstract
Segmentation of moving objects is the basic step for surveillance system. The Gaussian Mixture Model is one of the best models to cope with repetitive motions in a dynamic and complex environment. In this paper, an Adaptively Adjustment Mechanism was proposed by fully utilizing Gaussian distributions with least number so as to save the amount of computation. In addition to that, by applying proposed Gaussian Mixture Model scheme to edge segmented image and combining with data fusion method, the proposed algorithm was able to resist illumination change in scene and remove shadows of motion. Experiments proved the excellent performance.
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Huang, T., Fang, X., Qiu, J., Ikenaga, T. (2010). Adaptively Adjusted Gaussian Mixture Models for Surveillance Applications. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_70
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DOI: https://doi.org/10.1007/978-3-642-11301-7_70
Publisher Name: Springer, Berlin, Heidelberg
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