Visual Tracking via Locality-sensitive Kernel Sparse Representation
HUANG Hongtu①③ BI Duyan① GAO Shan① ZHA Yufei① HOU Zhiqiang②
①(Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an 710038, China) ②(Information and Navigation Institute, Air Force Engineering University, Xi’an 710077, China) ③(95972 Troops of PLA, Jiuquan 735018, China)
In order to solve the problem of lack of discriminability in the l1-norm constraint sparse representation, visual tracking via locality-sensitive kernel sparse representation is proposed. To improve the linear discriminable power, the candidates’ Scale-Invariant Feature Transform (SIFT) is mapped into high dimension kernel space using the Gaussian kernel function. The locality-sensitive kernel sparse representation is acquired in the kernel space. The candidates’ representation are obtained after multi-scale maximum pooling. Finally, the candidates’ representation is put into the classifier and the candidate with the biggest Support Vector Machines (SVMs) score is recognized as the target. And the experiments demonstrate that the robustness of the proposed algorithm is improved due to the use of the data locality under the kernel sparse representation.
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