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Graph mode-based contextual kernels for robust SVM tracking
2011
2011 International Conference on Computer Vision
Visual tracking has been typically solved as a binary classification problem. Most existing trackers only consider the pairwise interactions between samples, and thereby ignore the higher-order contextual interactions, which may lead to the sensitivity to complicated factors such as noises, outliers, background clutters and so on. In this paper, we propose a visual tracker based on support vector machines (SVMs), for which a novel graph mode-based contextual kernel is designed to effectively
doi:10.1109/iccv.2011.6126364
dblp:conf/iccv/LiDWSH11
fatcat:7irp5yeg7jhujoetzjus3ptn7e