Abstract
Background suppression in video sequences has attracted growing attention and is one of the heated issues in almost every task of video processing. An online fuzzy clustering for automatic background suppression is presented in this paper. First, in the classical fuzzy clustering methods, we have to wait until all data have been generated before the learning process begins. It is impractical because in real application for background suppression, the video length is unknown and the video frames are generated dynamically in a streaming environment and arrive one at a time. Our method has an ability to adapt and change through complex scenes in a true online fashion. Secondly, different from previous works for background suppression, where the information of the detected background is ignored, we propose a new way to incorporate this information. Finally, to estimate the model parameters, the scoring method is adopted to minimize the fuzzy objective function with the Kullback-Leibler divergence information. Experiments on real datasets are presented. The performance of the proposed model is compared to that of other background modeling techniques, demonstrating the robustness and accuracy of our method.
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This research has been supported in part by the Canada Research Chair Program and the NSERC Discovery grant.
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Nguyen, T.M., Wu, Q.M.J., Mukherjee, D. (2015). An Online Adaptive Fuzzy Clustering and Its Application for Background Suppression. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_17
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