基于局部敏感核稀疏表示的视频跟踪

黄宏图; 毕笃彦; 高山; 查宇飞; 侯志强

doi:10.11999/JEIT150785

基于局部敏感核稀疏表示的视频跟踪

doi: 10.11999/JEIT150785

1.
(空军工程大学航空航天工程学院西安 710038) ②(空军工程大学信息与导航学院西安 710077) ③(中国人民解放军95972部队酒泉 735018)

基金项目:

国家自然科学基金(61175029, 61379104, 61372167)，国家自然科学基金青年科学基金(61203268, 61202339)

计量
- 文章访问数: 1300
- HTML全文浏览量: 105
- PDF下载量: 517
- 被引次数: 0
出版历程
- 收稿日期: 2015-06-29
- 修回日期: 2015-11-27
- 刊出日期: 2016-04-19

Visual Tracking via Locality-sensitive Kernel Sparse Representation

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)

摘要

摘要: 为了解决l1范数约束下的稀疏表示判别信息不足的问题，该文提出基于局部敏感核稀疏表示的视频目标跟踪算法。为了提高目标的线性可分性，首先将候选目标的SIFT特征通过高斯核函数映射到高维核空间，然后在高维核空间中求解局部敏感约束下的核稀疏表示，将核稀疏表示经过多尺度最大值池化得到候选目标的表示，最后将候选目标的表示代入在线的SVMs，选择分类器得分最大的候选目标作为目标的跟踪位置。实验结果表明，由于利用了核稀疏表示下数据的局部性信息，使得算法的鲁棒性得到一定程度的提高。
- 视频跟踪 /
- 核稀疏表示 /
- 局部敏感约束 /
- 支持向量机
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)

HTML全文

参考文献(18)

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.

施引文献

资源附件(0)

访问统计