Abstract
The Mixture of Gaussians (MoG) is a frequently used method for foreground-background separation. In this paper, we propose an on-line learning framework that allows the MoG algorithm to quickly adapt its localized parameters. Our main contributions are: local parameter adaptations, a feedback based updating method for stopped objects, and hierarchical SURF features matching based ghosts and local illumination suppression method. The proposed model is rigorously tested and compared with several previous models on BMC data set and has shown significant performance improvements. © 2013 Springer-Verlag.
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CITATION STYLE
Shah, M., Deng, J., & Woodford, B. (2013). Illumination invariant background model using mixture of Gaussians and SURF features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7728 LNCS, pp. 308–314). https://doi.org/10.1007/978-3-642-37410-4_27
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