基于局部敏感核稀疏表示的視頻跟蹤
doi: 10.11999/JEIT150785 cstr: 32379.14.JEIT150785
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1.
(空軍工程大學航空航天工程學院 西安 710038) ②(空軍工程大學信息與導航學院 西安 710077) ③(中國人民解放軍95972部隊 酒泉 735018)
基金項目:
國家自然科學基金(61175029, 61379104, 61372167)，國家自然科學基金青年科學基金(61203268, 61202339)
Visual Tracking via Locality-sensitive Kernel Sparse Representation
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2.
(Information and Navigation Institute, Air Force Engineering University, Xi&rsquo
Funds:
The National Natural Science Foundation of China (61175029, 61379104, 61372167), The Young Scientists Fund of the National Natural Science Foundation of China (61203268, 61202339)
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摘要: 為了解決l1范數約束下的稀疏表示判別信息不足的問題，該文提出基于局部敏感核稀疏表示的視頻目標跟蹤算法。為了提高目標的線性可分性，首先將候選目標的SIFT特征通過高斯核函數映射到高維核空間，然后在高維核空間中求解局部敏感約束下的核稀疏表示，將核稀疏表示經過多尺度最大值池化得到候選目標的表示，最后將候選目標的表示代入在線的SVMs，選擇分類器得分最大的候選目標作為目標的跟蹤位置。實驗結果表明，由于利用了核稀疏表示下數據的局部性信息，使得算法的魯棒性得到一定程度的提高。Abstract: In order to solve the problem of lack of discriminability in thel1-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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