An Object Tracking Method Based on Illumination Compensation

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Abstract:

Aiming at the illumination change and partial occlusion in the object tracking, an object tracking method based on illumination compensation was proposed. An illumination compensation method based on Retinex was applied to the sequence images, a structural appearance model and template matching were used to track the object. Dense sampling was used to obtain candidates, extended least median square was used to match templates, and a step by step template updating method is applied. The experimental results demonstrate the effect of the proposed method.

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286-289

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March 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] Amit A., Ehud R., Ilan S.: Robust fragments-based tracking using the integral histogram. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2006). New York, USA, 17-22 June 2006, 793-805.

DOI: 10.1109/cvpr.2006.256

Google Scholar

[2] David A. R., Jongwoo L., Ruei S. L., Ming H. Y.: Incremental Learning for Robust Visual Tracking. J. International Journal of Computer Vision. 2008, 77, 125-141.

Google Scholar

[3] Black M., Jepson A.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. J. International Journal of Computer Vision. 1998, 26(1), 63-84.

DOI: 10.1007/bfb0015548

Google Scholar

[4] Grabner H., Leisner C., Bischof H.: Semi-supervised on-line boosting for robust tracking. In European Conference on Computer Vision. Marseille, France, 2008, 234-247.

DOI: 10.1007/978-3-540-88682-2_19

Google Scholar

[5] Babenko B., Yang M., Belongie S.: Robust object tracking with online multiple instance learning. J. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011, 33(8), 1619-1632.

DOI: 10.1109/tpami.2010.226

Google Scholar

[6] Kalal Z., Matas J., Mikolajczyk K.: P-N learning: bootstrapping binary classifiers by structural constraints. In IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA, 2010, 49-56.

DOI: 10.1109/cvpr.2010.5540231

Google Scholar

[7] Avidan S.: Support vector tracking. J. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004, 26(8), 1061-1072.

DOI: 10.1109/tpami.2004.53

Google Scholar

[8] Zhang K.H., Zhang L., Yang M. H.: Real-time compressive tracking. In European Conference on Computer Vision. Firenze, Italy, 2012, 866-879.

Google Scholar

[9] B. Babenko, M. H. Yang, S. Belongie. Visual tracking with online weighted multiple instance learning. In CVPR, (2009).

Google Scholar

[10] Mei X., Ling H.: Robust Visual tracking and vehicle classification via sparse representation. J. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011, 33(11), 2259-2272.

DOI: 10.1109/tpami.2011.66

Google Scholar

[11] 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.

DOI: 10.1016/j.patrec.2011.03.010

Google Scholar

[12] Jia X., Lu H., Yang M.: Visual tracking via adaptive structural local sparse appearance model. In Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, 16-21 June 2012, 1822-1829.

DOI: 10.1109/cvpr.2012.6247880

Google Scholar