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Journal of Applied Sciences >

2015 , Vol. 33 >Issue 5: 502 - 517

DOI: https://doi.org/10.3969/j.issn.0255-8297.2015.05.005

Signal and Information Processing

Multi-feature Selection Tracking Based on Support Vector Machine

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  • 1. School of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
    2. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing 210044, China;
    3. 94804 PLA Troops, Shanghai 200434, China;
    4. Suzhou Electrical Apparatus Science Research Institute Co., Ltd, Suzhou 215000, Jiangsu Province, China

Received date: 2015-04-21

  Revised date: 2015-05-26

  Online published: 2015-09-30

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Abstract

Discriminative tracking is generally based on a single feature and uses the current track result (a positive sample) and some negative samples to train the classifier. It may lead to tracking due to occlusion, illumination changes and deformation. To overcome the problem caused by single feature description and single positive sample training, we propose a multi-feature selection algorithm based on the support vector machine (SVM). The classifier is trained with multiple positive and negative samples based on multi-feature descriptions. In the tracking step, a candidate sample with maximum confidence probability is used as the tracking result. Further, we take advantage of a subspace learning method to update positive samples used in the classifier training. Experimental results show that the proposed algorithm has desirable performance in a variety of challenging situations.

Key words： classifier; object tracking; support vector machine (SVM); subspace learning; multi-feature selection

Cite this article

HU Zhao-hua, XU Yu-wei, ZHAO Xiao-lei, HE Jun, ZHOU You . Multi-feature Selection Tracking Based on Support Vector Machine[J]. Journal of Applied Sciences, 2015 , 33(5) : 502 -517 . DOI: 10.3969/j.issn.0255-8297.2015.05.005

References

[1] Yilmaz A, Javed O, Shah M. Object tracking: a survey [J]. ACM Computing Surveys, 2006, 38(4): 1-45.

[2] Zhang S P, Yao H X, Sun X. Sparse coding based visual tracking: review and experimental comparison [J]. Pattern Recognition, 2013, 46 (7): 1772-1788.

[3] Wang Z H, Yoon S, Xie S J, Lu Y, Rark D S. Visual tracking with semi-supervised online weighted multiple instance learning [J]. The Visual Computer, 2015: 1-14.

[4] Yi K M, Jeong H, Lee B, Choi J Y. Visual tracking in complex scenes through pixel-wise tri-modeling [J]. Machine Vision and Applications, 2015: 1-13.

[5] Yang X, Wang M, Tao D C. Robust visual tracking via multi-graph ranking [J]. Neurocomputing, 2015.

[6] Zhong B N, Shen Y J, Chen Y. Online learning 3D context for robust visual tracking [J]. Neurocomputing, 2015, 151: 710-718.

[7] Ma C, Liu C C. Two dimensional hashing for visual tracking [J]. Computer Vision and Image Understanding, 2015.

[8] Li H X, Li Y, Porikli F. Deep track: learning discriminative feature representations online for robust visual tracking [J]. arXiv preprint arXiv: 1503. 00072, 2015.

[9] Hwang J P, Baek J, Choi B, Kim E. A novel part-based approach to mean-shift algorithm for visual tracking [J]. International Journal of Control, Automation and Systems, 2015: 1-11.

[10] Wang L J, Lu H C, Wang D. Visual tracking via structure constrained grouping [J]. Signal Processing Letters, 2015, 7(22): 794-798.

[11] Zeng F X, Liu X, Huang Z T, Ji Y F. Kernel based multiple cue adaptive appearance model for robust real-time visual tracking [J]. IEEE Signal Processing Letters, 2013, 20(11): 1094-1097.

[12] Yu Q, Dinh T B, Medioni G. Online tracking and reacquistion using co-trained generative and discriminative trackers [C]//Proceedings of 10th European Conference on Computer Vision, Marseille: Springer Berlin Heidelberg, 2008, 5303: 678-691.

[13] Bai T X, Li Y F. Robust visual tracking with structured sparse representation appearance model[J]. Pattern Recognition, 2012, 45(6): 2390-2404.

[14] Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking [J]. International Journal of Computer Vision, 2008, 77 (1/3): 125-141.

[15] Mei X, Ling H B. Robust visual tracking using L1 minimization [C]//Proceedings of 2009 IEEE 12th International Conference on Computer Vision, Kyoto: IEEE, 2009: 1436-1443.

[16] Wang D, Lu H C, Yang M H. Online object tracking with sparse prototypes [J]. IEEE Transctions on Image Processing, 2013, 22(1): 314-325.

[17] Zhang T Z, Ghanem1 B, Liu S, Ahuja N. Low-rank sparse learning for robust visual tracking[C]// Proceedings of 12th European Conference on Computer Vision, Florence:, Springer Berlin Heidelberg, 2012, 7577: 470-484.

[18] Grabner H, Leistner C, Bischof H. Semi-supervised on-line boosting for robust tracking[C]//Proceedings of 10th European Conference on Computer Vision, Marseille: Springer Berlin Heidelberg, 2008, 5302: 234-247.

[19] Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami: IEEE, 2009: 983-990.

[20] Zhang K H, Zhang L, Yang M H. Real-time compressive tracking [C]//Proceedings of 12th European Conference on Computer Vision, Florence:, Springer Berlin Heidelberg, 2012: 864-877.

[21] Zhang K H, Zhang L, Yang M H. Real-time object tracking via online discriminative feature selection [J]. IEEE Transactions on Image Processing, 2013, 22(12): 4664-4677.

[22] Avidan S. Support vector tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26: 1064-1072.

[23] Collins R, Liu Y X. Leordeanu M. Online selection of discriminative tracking features [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27: 1631-1643.

[24] Grabner H, Grabner M, Bischof H. Real-time tracking via online boosting [C]//Proceedings of 2006 Proceedings of the British Machine Vision Conference, Edinburgh, 2006.

[25] Gammermann A. Support vector machine learning algorithm and transduction [J]. Computational Statistics, 2000, 15(1): 31-39.

[26] Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012: 1822-1829.

[27] Ojala T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

[28] Khan Z, Batch T, Dellaert F. A Rao–blackwellized particle filter for eigen-tracking[C]//Proceedings of 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, 2004, 2: 980-986.

[29] Zhou S K, Chellappa R, Moghaddam B. Visual tracking and recognition using appearanceadaptive models in particle filters [J]. IEEE Transactions on Image Processing, 2004, 13(11): 1491-1506.

[30] Yuan X T, Liu X B, Yan S C. Visual classification with multitask joint sparse representation[J]. IEEE Transactions on Image Processing, 2012, 21(10): 4349-4360.

[31] John C P. Sequential minimal optimization: a fast algorithm for training support vector machines[J]. [S.l.]: MIT Press, 1998: 1-21.

[32] Olson C F. Maximum-likelihood template matching [C]//Proceedings of 2000 IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, 2000, 2: 52-57.

[33] Matthews I, Ishikawa T, Baker S. The template update problem [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26 (6): 810-815.

[34] Bao C L, Wu Y, Ling H B. Real time robust `1 tracker using accelerated proximal gradient approach [C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, IEEE, 2012: 1830-1837.

[35] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.

[36] Kwon J, Lee K M. Visual tracking decomposition [C]//Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010: 1269-1276.

[37] Adam A, Rivlin E, Shimshoni T. Robust fragments-based tracking using the integral histogram[C]//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, 1: 798-805.

[38] Zhang T Z, Ghanem B, Liu S, Et A L .Robust visual tracking via structured multi-task sparse learning [J]. International Journal of Computer Vision, 2013, 101(2): 367-383.

[39] Liu B Y, Huang J Z, Yang L, Kulikowsk C. Robust tracking using local sarse appearance model and k-selection [C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2011: 1313-1320.

[40] Zhong W, Lu H C, Yang M H. Robust object tracking via sparsity-based collaborative model[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012: 1838-1845.

[41] Wu Y, Lim J, Yang M H. Online object tracking: a benchmark [C]//2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2411-2418.
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