一种鲁棒的基于集成学习的核相关红外目标跟踪算法

doi:10.11999/JEIT170527

Abstract
Figure/Table
References (20)
Related Citation (15)

Download: PDF (10745 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the infrared object tracking, the single classifier is not enough to fit the multimodal data due to the complex background information of the target and the significant change in the appearance. In this paper, Kernelized Correlation Filters (KCF) tracking algorithm is used to integrate kernelized correlation classifiers into one framework through ensemble learning. It uses the KCF classifier that has analytical solutions to balance the contradiction between the robustness and instantaneity, thereby addressing the complex background and significant appearance changes, and consequently significantly improving the tracking performance and stability. To verify the effectiveness of the algorithm, this paper uses two kernelized correlation trackers to learn a strong classifier. The qualitative and quantitative experiments show that the proposed algorithm outperforms the traditional KCF algorithm, and the tracking speed is superior to most of the comparison algorithms.

Key words： Object tracking Ensemble learning Discriminant classifier Kernelized correlation tracking

Received: 31 May 2017 Published: 11 January 2018

PACS:

TP391

Fund:The Ministry of Education-China Mobile Research Fund Project (MCM20160405)

Corresponding Authors: WU Ensi E-mail: grabertao@gmail.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XIE Tao
	WU Ensi

Cite this article:

XIE Tao,WU Ensi. A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning[J]. JEIT, 2018, 40(3): 602-609.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT170527 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I3/602

[1]	李少毅, 梁爽, 张凯, 等. 基于红外压缩成像的点目标跟踪方法研究[J]. 电子与信息学报, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT141324.
	LI Shaoyi, LIANG Shuang, ZHANG Kai, et al. Research of infrared compressive imaging based point target tracking method[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT 141324.
[2]	袁广林, 薛模根. 基于稀疏稠密结构表示与在线鲁棒字典学习的视觉跟踪[J]. 电子与信息学报, 2015, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
	YUAN Guanglin and XUE Mogen. Visual tracking based on sparse dense structure representation and online robust dictionary learning[J]. Journal of Electronics & Information Technology, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
[3]	罗会兰, 钟宝康, 孔繁胜. 带权分块压缩感知的预测目标跟踪算法[J]. 电子与信息学报, 2015, 37(5): 1160-1166. doi: 10.11999/JEIT140997.
	LUO Huilan, ZHONG Baokang, and KONG Fansheng. Tracking using weighted block compressed sensing and location prediction[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1160-1166. doi: 10.11999/JEIT 140997.
[4]	ROSS D A, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141. doi: 10.1007/s11263-007-0075 -7.
[5]	ZHONG W, LU H, and YANG M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368. doi: 10.1109/TIP.2014.2313227.
[6]	薛一哲, 王拓. 基于代价敏感 Adaboost 目标跟踪[J]. 中国图像图形学报, 2016, 21(5): 544-555.
	XUE Yizhe and WANG Tuo. Object tracking based on cost-sensitive Adaboost algorithm[J]. Chinese Journal of Image and Graphics, 2016, 21(5): 544-555.
[7]	ZHANG K and SONG H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411. doi: 10.1016/j.patcog.2012.07.013.
[8]	BABENKO B, YANG M H, and 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.
[9]	HU J, LU J, and TAN Y P. Deep metric learning for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(11): 2056-2068. doi: 10.1109/ TCSVT.2015.2477936.
[10]	KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. Proceedings of the Advances in Neural Information Processing Systems, Nevada, USA, 2012: 1097-1105. doi: 10.1145/3065386.
[11]	TANG Z, WANG S, HUO J, et al. Bayesian framework with non-local and low-rank constraint for image reconstruction [C]. Proceedings of the Journal of Physics, 2017, 787: 012008. doi: 10.1088/1742-6596/787/1/012008.
[12]	HENRIQUES J F, CASEIRO R, MARTINS P, et al. High- speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. doi: 10.1109/TPAMI.2014.2345390.
[13]	LIU T, WANG G, and YANG Q. Real-time part-based visual tracking via adaptive correlation filters[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 4902-4912. doi: 10.1109/ CVPR.2015.7299124.
[14]	MATTHEWS L, ISHIKAWA T, and BAKER S. The template update problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6): 810-815. doi: 10.1109/TPAMI.2004.16.
[15]	侯志强, 黄安奇, 余旺盛, 等. 基于局部分块和模型更新的视觉跟踪算法[J]. 电子与信息学报, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT141134.
	HOU Zhiqiang, HUANG Anqi, YU Wangsheng, et al. Visual object tracking method based on local patch model and model update[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT 141134.
[16]	薛模根, 朱虹, 袁广林. 基于在线判别式字典学习的鲁棒视觉跟踪[J]. 电子与信息学报, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325.
	XUE Mogen, ZHU Hong, and YUAN Guanglin. Robust visual tracking based on online discrimination dictionary learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT 141325.
[17]	苏巧平, 刘原, 卜英乔, 等. 基于稀疏表达的多示例学习目标追踪算法[J]. 计算机工程, 2013, 39(3): 213-217.
	SHU Qiaoping, LIU Yuan, BU Yingqiao, et al. Multi-example learning target tracking algorithm based on sparse expression [J]. Computer Engineering, 2013, 39(3): 213-217.
[18]	VIOLA P, JONES M J, and SNOW D. Detecting pedestrians using patterns of motion and appearance[J]. International Journal of Computer Vision, 2005, 63(2): 153-161. doi: 10.1007/s11263-005-6644-8.
[19]	KALAL Z, MIKOLAJCZYK K, and MATAS J. Tracking- learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422. doi: 10.1109/TPAMI.2011.239
[20]	ZHANG K, ZHANG L, LIU Q, et al. Fast visual tracking via dense spatio-temporal context learning[C]. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141. doi: 10.1007/978-3-319-10602- 1_9.