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Target detection and tracking technique is one of the hot subjects in image processing and computer vision fields, which has significant research value not only in military areas such as imaging guidance and military target tracking, but also for civil use such as security and monitoring and the intelligent man-machine interaction. In this paper, for target deformation, scale changing, rotation, and other issues in the long-term stable target tracking, a bootstrapping feedback learning algorithm is proposed, which may improve the target model and the classifier discriminating capacity as well as the fault tolerance ability; and it also makes fewer errors during the updating, and then the proof of convergence of the algorithm is given. Experimental results show that among the same tracking algorithms, utilization of the learning method to update the target model and classifier is more stable and more adaptable than unusing it in the processes of target scale changing, deformation, rotation, perspective changing and fuzzy. And compared with the existing conventional method, this method has a better robustness, and a high value in practical application and research.
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Keywords:
- target tracking /
- target model update /
- semi-supervising learning /
- classification
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[2] Guo G R, Wang H Q, Jiang B 2006 Acta Phys. Sin. 55 3985
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[13] Song S, Zhang B, Yin C L 2014 Opt. Precision Eng. 22 1037 (in Chinese) [宋策, 张葆, 尹传历 2014 光学精密工程 22 1037]
[14] Grabner H, Bischof H 2006 CVPR 2
[15] Avidan S 2007 PAMI 29 261
[16] Collins R, Liu Y, 2005 PAMI 27 1631
[17] Lim J, Ross D, Lin R, Yang M 2005 NIPS 2 7
[18] Yu Q, Dinh T, Medioni G 2008 ECCV 3 6
[19] Kalal Z, Matas J, Mikolajczyk K 2010 Conference on Computer Vision and Pattern Recognition, CVPR, San Francisco, CA, USA
[20] Zhang T, Oles F J 2000 Proceedings of 17th International Conference on Machine Learning. Stanford 2000 p1191
[21] Nigam K, McCallum A, Thrun S, Mitchell T 2000 Machine Learning 39 103
[22] Blum A, Mitchell T 1998 COLT 1 2
[23] Xu Q, Hu D H, Xue H 2009 BMC Bioinformatics 10 S47
[24] Viola P, Jones M, Snow D 2005 International Journal of Computer Vision 63 153
[25] Breiman L 2001 Machine Learning 45 5
[26] Lepetit V, Fua P 2006 IEEE Trans. Pattern Analysis and Machine Intelligence 28 1465
[27] Grabner H, Leistner C, Bischof H 2008 European Conf. on Computer Vision
[28] Babenko B, Yang M H, Belongie S 2009 IEEE Conf. on Computer Vision and Pattern Recognition, Washington, DC 2009 p983
[29] Yu Q, Dinh T B, Medioni G 2008 European Conf. on Computer Vision 2008
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[1] Li T W, Shi A G, He S H 2009 Acta Phys. Sin. 58 794 (in Chinese) [李天伟, 石爱国, 何四华 2009 58 794]
[2] Guo G R, Wang H Q, Jiang B 2006 Acta Phys. Sin. 55 3985
[3] Wang M W, Zhai H C, Gao L J 2009 Acta Phys. Sin. 58 1662 (in Chinese) [王明伟, 翟宏琛, 高丽娟 2009 58 1662]
[4] Zhang J S, Zhang Z T 2010 Chinese Phys. B 19 104601
[5] Sun J F, Wang Q, Wang L 2010 Chinese Phys. B 19 104203
[6] Chen G Y, Guo Z X, Zhang C P 2003 Chin. Phys. Lett. 20 2161
[7] Wang L J, Jia S M, Wang S, Li Z X 2013 Opt. Precision Eng. 21 2364 (in Chinese) [王丽佳, 贾松敏, 王爽, 李秀智 2013 光学精密工程 21 2364]
[8] Zhu Q P, Yan J, Zhang H 2013 Opt. Precision Eng. 21 437 (in Chinese) [朱秋平, 颜佳, 张虎 2013 光学精密工程 21 437]
[9] Chen D C, Zhu M, Gao W, Sun H H, Yang W B 2014 Opt. Precision Eng. 22 1661 (in Chinese) [陈东成, 朱明, 高文, 孙宏海, 杨文波 2014 光学精密工程 22 1661]
[10] Ma Y, Lv Q B, Liu Y Y, Qian L L, Pei L L 2013 Acta Phys. Sin. 62 204202 (in Chinese) [马原, 吕群波, 刘扬阳, 钱路路, 裴琳琳 2013 62 204202]
[11] Duarte M F, Baraniuk R G 2012 IEEE Trans. Image Proc. 21 494
[12] Sun X Y, Chang F L 2013 Opt. Precision Eng. 21 3191 (in Chinese) [孙晓燕, 常发亮 2013 光学精密工程 21 3191]
[13] Song S, Zhang B, Yin C L 2014 Opt. Precision Eng. 22 1037 (in Chinese) [宋策, 张葆, 尹传历 2014 光学精密工程 22 1037]
[14] Grabner H, Bischof H 2006 CVPR 2
[15] Avidan S 2007 PAMI 29 261
[16] Collins R, Liu Y, 2005 PAMI 27 1631
[17] Lim J, Ross D, Lin R, Yang M 2005 NIPS 2 7
[18] Yu Q, Dinh T, Medioni G 2008 ECCV 3 6
[19] Kalal Z, Matas J, Mikolajczyk K 2010 Conference on Computer Vision and Pattern Recognition, CVPR, San Francisco, CA, USA
[20] Zhang T, Oles F J 2000 Proceedings of 17th International Conference on Machine Learning. Stanford 2000 p1191
[21] Nigam K, McCallum A, Thrun S, Mitchell T 2000 Machine Learning 39 103
[22] Blum A, Mitchell T 1998 COLT 1 2
[23] Xu Q, Hu D H, Xue H 2009 BMC Bioinformatics 10 S47
[24] Viola P, Jones M, Snow D 2005 International Journal of Computer Vision 63 153
[25] Breiman L 2001 Machine Learning 45 5
[26] Lepetit V, Fua P 2006 IEEE Trans. Pattern Analysis and Machine Intelligence 28 1465
[27] Grabner H, Leistner C, Bischof H 2008 European Conf. on Computer Vision
[28] Babenko B, Yang M H, Belongie S 2009 IEEE Conf. on Computer Vision and Pattern Recognition, Washington, DC 2009 p983
[29] Yu Q, Dinh T B, Medioni G 2008 European Conf. on Computer Vision 2008
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