基于局部分块和模型更新的视觉跟踪算法

侯志强; 黄安奇; 余旺盛; 刘翔

doi:10.11999/JEIT141134

基于局部分块和模型更新的视觉跟踪算法

doi: 10.11999/JEIT141134

基金项目:

国家自然科学基金(61175029, 61473309)和陕西省自然科学基金(2011JM8015)资助课题

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- 文章访问数: 1417
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- 被引次数: 0
出版历程
- 收稿日期: 2014-09-01
- 修回日期: 2014-11-02
- 刊出日期: 2015-06-19

Visual Object Tracking Method Based on Local Patch Model and Model Update

摘要

摘要: 针对目标跟踪过程中的目标表观变化、背景干扰及发生遮挡等问题，该文提出一种基于局部分块和模型更新的视觉跟踪算法。该文采用粗搜索与精搜索相结合的双层搜索方法来提高目标的定位精度。首先，在包含部分背景区域的初始跟踪区域内构建目标模型。然后，利用基于积分直方图的局部穷搜索算法初步确定目标的位置，接着在当前跟踪区域内通过分块学习来精确搜索目标的最终位置。最后，利用创建的模型更新域对目标模型进行更新。该文主要针对分块跟踪中的背景抑制、模型更新等方面进行了研究，实验结果表明该算法对目标表观变化、背景干扰及遮挡情况的处理能力都有所增强。
- 视觉跟踪 /
- 局部分块模型 /
- 穷搜索 /
- 局部分块学习 /
- 模型更新
Abstract: In order to solve the problems of appearance change, background distraction and occlusion in the object tracking, an efficient algorithm for visual tracking based on the local patch model and model update is proposed. This paper combines rough-search and precise-search to enhance the tracking precision. Firstly, it constructs the local patch model according to the initialized tracking area which includes some background areas. Secondly, the target is preliminarily located through the local exhaustive search algorithm based on the integral histogram, then the final position of the target is calculated through the local patches learning. Finally, the local patch model is updated with the retained sequence during the tracking process. This paper mainly studies the search strategy, background restraining and model update, and the experimental results show that the proposed method obtains a distinct improvement in coping with appearance change, background distraction and occlusion.
- Visual tracking /
- Local patch model /
- Exhaustive search /
- Local patches learning /
- Model update

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参考文献(16)

Yang Han-xuan, Shao Ling, Zheng Feng, et al.. Recent advances and trends in visual tracking: a review[J]. Neurocomputing, 2011, 74(18): 3823-3831.

Wu Yi, Lim J, and Yang M H. Online object tracking: a benchmark[C]. Proceedings of the Computer Vision and Pattern Recognition, Portland, United States, 2013: 2411-2418.

Smeulders A W M, Chu D M, Cucchiara R, et al.. Visual tracking: an experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013: DOI: 10. 1109/TPAMI. 2013. 230.

Lu Zhang and Laurens V D M. Structure preserving object tracking[C]. Proceedings of the Computer Vision and Pattern Recognition, Portland, United States, 2013: 1838-1845.

Comaniciu D, Ramesh V, and Meer P. Real-time tracking of non-rigid objects using mean shift[C]. Proceedings of the Computer Vision and Pattern Recognition, Hilton Head Island, United States, 2000: 142-149.

Comaniciu D, Ramesh V, and Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577.

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.

Wang Dong, Lu Hu-chuan, and Yang M H. Online object tracking with sparse prototypes[J]. IEEE Transactions on Image Processing, 2013, 22(1): 314-315.

Adam A, Rivlin E, and Shimshoni I. Robust fragments-based tracking using the integral histogram[C]. Proceedings of the Computer Vision and Pattern Recognition, New York, United States, 2006: 798-805.

Nejhum S, Ho J, and Yang M H. Online visual tracking with histograms and articulating blocks[J]. Computer Vision and Image Understanding, 2010, 114(8): 901-914.

董文会, 常发亮, 李天平. 融合颜色直方图及SIFT特征的自适应分块目标跟踪方法[J]. 电子与信息学报, 2013, 35(4): 770-776.

Dong Wen-hui, Chang Fa-liang, and Li Tian-ping. Adaptive fragments-based target tracking method fusing color histogram and SIFT features[J]. Journal of Electronics Information Technology, 2013, 35(4): 770-776.

Wang Shu, Lu Hu-chuan, Yang Fan, et al.. Superpixel tracking[C]. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain, 2011: 1323-1330.

Yang Fan, Lu Hu-chuan, and Yang M H. Robust superpixel tracking[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1639-1651.

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

Porkili F. Integral histogram: a fast way to extract histograms in cartesian spaces[C]. Proceedings of the Computer Vision and Pattern Recognition, San Diego, United States, 2005: 829-836.

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