Visual Tracking via Locality-sensitive Kernel Sparse Representation

HUANG Hongtu; BI Duyan; GAO Shan; ZHA Yufei; HOU Zhiqiang

doi:10.11999/JEIT150785

Volume 38 Issue 4

Apr. 2016

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HUANG Hongtu, BI Duyan, GAO Shan, ZHA Yufei, HOU Zhiqiang. Visual Tracking via Locality-sensitive Kernel Sparse Representation[J]. Journal of Electronics & Information Technology, 2016, 38(4): 993-999. doi: 10.11999/JEIT150785

Citation:

HUANG Hongtu, BI Duyan, GAO Shan, ZHA Yufei, HOU Zhiqiang. Visual Tracking via Locality-sensitive Kernel Sparse Representation[J]. Journal of Electronics & Information Technology, 2016, 38(4): 993-999. doi: 10.11999/JEIT150785

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Visual Tracking via Locality-sensitive Kernel Sparse Representation

doi: 10.11999/JEIT150785

1.
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)

Received Date: 2015-06-29
Rev Recd Date: 2015-11-27
Publish Date: 2016-04-19

Abstract

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.
- Visual tracking,
- Kernel sparse representation,
- Locality-sensitive constraint,
- Support Vector Machine (SVM)

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References(18)

References

WU Yi, LIM J, and YANG Minghsuan. Object tracking Benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(6): 1442-1456.

WRIGHT J, MA Yi, MAIRAL J, et al. Sparse representation for computer vision and pattern recognition[J]. Proceedings of the IEEE, 2010, 98(6): 1031-1044.

MEI X and LING H. Robust visual tracking using L1 minimization[C]. 2009 IEEE 12th International Conference on Computer Vision, Kyoto, 2009: 1436-1443.

BAO Chenglong, WU Yi, LING Haibin, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 1830-1837.

ZHONG Wei, LU Huchuan, and YANG Minghsuan. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368.

WANG N and YEUNG D Y. Learning a deep compact image representation for visual tracking[C]. Advances in Neural Information Processing Systems, Nevada, 2013: 809-817.

GAO Jin, LING Haibin, HU Weiming, et al. Transfer Learning Based Visual Tracking with Gaussian Processes Regression[M]. Computer Vision-ECCV 2014, Zurich: Springer International Publishing, 2014: 188-203.

王瑞, 杜林峰, 孙督, 等. 复杂场景下结合SIFT与核稀疏表示的交通目标分类识别[J]. 电子学报, 2014, 42(11): 2129-2134.

WANG Rui, DU Linfeng, SUN Du, et al. Traffic object recognition in complex scenes based on SIFT and kernel sparse representation[J]. Acta Electronica Sinica, 2014, 42(11): 2129-2134.

YU K, ZHANG T, and GONG Y. Nonlinear learning using local coordinate coding[C]. Advances in Neural Information Processing Systems. Vancouver, 2009: 2223-2231.

LI Feifei, FERGUS R, and PERONA P. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories[J]. Computer Vision and Image Understanding, 2007, 106(1): 59-70.

WANG Qing, FENG Chen, YANG Jimei, et al. Transferring visual prior for online object tracking[J]. IEEE Transactions on Image Processing, 2012, 21(7): 3296-3305.

LEE H, BATTLE A, RAINA R, et al. Efficient sparse coding algorithms[C]. Advances in Neural Information Processing Systems, Vancouver, 2006: 801-808.

GAO Shenghua, TSANG I W, and CHIA Liangtien. Sparse representation with kernels[J]. IEEE Transactions on Image Processing, 2013, 22(2): 423-434.

WANG Jinjun, YANG Jianchao, YU Kai, et al. Locality-constrained linear coding for image classification[C]. 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA. 2010: 3360-3367.

CHANG Chihchung and LIN Chihjen. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-27.

SMOLA A J and SCHOLKOPF B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222.

ROSS M A, LIM Jongwoo, LIN Ruei-Sung, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1/3): 125-141.

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