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Robust Visual Tracking via Multiple Kernel Boosting With Affinity Constraints
2014
IEEE transactions on circuits and systems for video technology (Print)
We propose a novel algorithm by extending the multiple kernel learning framework with boosting for an optimal combination of features and kernels, thereby facilitating robust visual tracking in complex scenes effectively and efficiently. While spatial information has been taken into account in conventional multiple kernel learning algorithms, we impose novel affinity constraints to exploit the locality of support vectors from a different view. In contrast to existing methods in the literature,
doi:10.1109/tcsvt.2013.2276145
fatcat:z6x4qozsj5dytgstpdvphy4w4u