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| Object Tracking Method Based on Particle Filter and Sparse Representation |
| YANG Da-Wei1,2,3,CONG Yang1,TANG Yan-Dong1 |
1.State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,
Shenyang 110016
2.University of the Chinese Academy of Sciences,Beijing 100049
3.School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159 |
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Abstract Aiming at the problem of illumination variation in the object tracking of video image sequence,an object tracking method which uses sparse representation in particle filter frame is proposed based on LBP textual feature of object. The tracking particles of the current frame are generated by the last tracking result according to Gaussian distribution,the sparse representation of each particle to the template subspace is obtained by solving the l1-regularized least squares problem,and the tracking object is determined. Then,particle filter is used to propagate sample distribution in next tracking frame. In the procedure,the template is updated using a new template updating strategy. The experimental results validate the performance and advancement of the proposed method.
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Received: 10 May 2012
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[1] Tsagkatakis G,Savakis A. Online Distance Metric Learning for Object Tracking. IEEE Trans on Circuits and Systems for Video Technology,2011,21(12): 1810-1821
[2] Hou Zhiqiang,Han Chongzhao. A Survey of Visual Tracking. Acta Automatica Sinica,2006,32(4): 603-617 (in Chinese)
(侯志强,韩崇昭.视觉跟踪技术综述.自动化学报,2006,32(4): 603-617)
[3] Comaniciu D,Ramesh V,Meer P. Kernel-Based Object Tracking.IEEE Trans on Pattern Analysis and Machine Intelligence,2003,25(5): 564-577
[4] Yim J,Jeong S,Gwon K,et al. Improvement of Kalman Filters for WLAN Based Indoor Tracking. Expert Systems with Applications,2010,37(1): 426-433
[5] Bugallo M F,Xu S S,Djuri P M. Performance Comparison of EKF and Particle Filtering Methods for Maneuvering Targets. Digital Signal Processing,2007,17(4): 774-786
[6] Wang Zhaowen,Yang Xiaokang,Xu Yi,et al. CamShift Guided Particle Filter for Visual Tracking. Pattern Recognition Letters,2009,30(4): 407-413
[7] Avidan S. Support Vector Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence,2004,26(8): 1064-1072
[8] Avidan S. Ensemble Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(2): 261-271
[9] Xiao Jinwei,Wu Gangshan. A Robust and Compact Descriptor Based on Center-Symmetric LBP // Proc of the 6th International Conference on Image and Graphics. Hefei,China,2011: 388-393
[10] Mei Xue,Ling Haibin. Robust Visual Tracking Using l1 Minimization // Proc of the IEEE International Conference on Computer Vision. Kyoto,Japan,2009: 1436-1443
[11] Li Peihua,Zhang Tianwen,Pece E C. Visual Contour Tracking Based on Particle Filter. Image and Vision Computing,2003,21(1): 111-123
[12] Zhang Chuntao,Guo Jiao,Xu Jialiang. Semi-Supervised Dimensionality Reduction Based on Sparsity Representation. Computer Engineering and Applications,2011,47 (20): 181-183 (in Chinese)
(张春涛,郭 皎,徐家良.基于稀疏表示的半监督降维方法.计算机工程与应用,2011,47(20): 181-183)
[13] Ojala T,Pietikinen M,Menp T. Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns // Proc of the 6th European Conference on Computer Vision. London,UK,2000: 404-420
[14] Wright J,Yang A Y,Ganesh A,et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(2): 210-227
[15] Kim S J,Koh K,Lustig M,et al. An Interior-Point Method for Large-Scale l1-Regularized Least Squares. IEEE Journal on Selected Topics in Signal Processing,2007,1(4): 606-617
[16] Porikli F,Tuzel O,Meer P. Covariance Tracking Using Model Update Based on Lie Algebra // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York,USA,2006,Ⅰ: 728-735
[17] Fan Baojie,Du Yingkui,Zhu Linlin,et al. A Robust Template Tracking Algorithm with Weighted Active Drift Correction. Pattern Recognition Letters,2011,32(9): 1317-1327 |
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2013, Vol. 26 
