目标跟踪中目标模型更新问题的半监督学习算法研究

高文; 汤洋; 朱明

doi:10.7498/aps.64.014205

摘要

本文针对长期稳定的目标跟踪中的目标形变、尺度缩放、旋转等问题, 提出一种步步为营的反馈式学习方法, 该方法通过正、负约束实现对于目标模型和分类器的判别能力和容错能力提高的同时, 使更新带来的误差尽量小, 并证明了其收敛性. 通过实验表明, 对于同一种跟踪算法使用本文提出的目标更新方法进行更新学习的比不更新学习的跟踪效果要稳定得多, 对于目标的尺度变化、形变、旋转、视角变化、模糊等都有较好的适应性, 并通过与现有的较流行的方法进行比较, 本文方法鲁棒性较好, 有很高的研究和应用价值.

关键词:

Abstract

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.

Keywords:

作者及机构信息

1.
中国科学院长春光学精密机械与物理研究所, 长春 130033;

2.
中国科学院航空光学成像与测量重点实验室, 长春 130033

基金项目: 中国科学院航空光学成像与测量重点实验室开放基金(批准号: Y2HC1SR121)资助的课题.

Authors and contacts

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;

2.
Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences, Changchun 130033, China

Funds: Project supported by the Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences (Grant No. Y2HC1SR121).

参考文献

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[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

施引文献

[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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