基于快速傅里叶变换的局部分块视觉跟踪算法

侯志强; 张浪; 余旺盛; 许婉君

doi:10.11999/JEIT150183

基于快速傅里叶变换的局部分块视觉跟踪算法

doi: 10.11999/JEIT150183

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)

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- 文章访问数: 1372
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- 被引次数: 0
出版历程
- 收稿日期: 2015-02-02
- 修回日期: 2015-06-03
- 刊出日期: 2015-10-19

Local Patch Tracking Algorithm Based on Fast Fourier Transform

Funds:

The National Natural Science Foundation of China (61175029, 61473309)

摘要

摘要: 针对视觉跟踪中目标表观变化、局部遮挡、背景干扰等问题，该文提出一种基于快速傅里叶变换的局部分块视觉跟踪算法。通过建立目标分块核岭回归模型并构建循环结构矩阵进行分块穷搜索来提高跟踪精度，利用快速傅里叶变换将时域运算变换到频域运算提高跟踪效率。首先，在包含目标的初始跟踪区域建立目标分块核岭回归模型；然后，提出通过构造循环结构矩阵进行分块穷搜索，并构建目标分块在相邻帧位置关系模型；最后，利用位置关系模型精确估计目标位置并进行分块模型更新。实验结果表明，该文算法不仅对目标表观变化、局部遮挡以及背景干扰等问题的适应能力有所增强，而且跟踪实时性较好。
- 视觉跟踪 /
- 核岭回归模型 /
- 快速傅里叶变换 /
- 分块穷搜索 /
- 位置关系模型
Abstract: In order to solve the problems of appearance change, local occlusion and background distraction in the visual tracking, a local patch tracking algorithm based on Fast Fourier Transform(FFT)is proposed. The tracking precision can be improved by establishing objects patch kernel ridge regression model and using patch exhaustive search based on circular structure matrix, and the efficiency can be improved by transforming time domains operation into frequency domains based on FFT. Firstly, patch kernel ridge regression model is constructed according to the initialized tracking area. Secondly, a patch exhaustive search method based on circular structure matrix is proposed, then the position model is constructed in adjoining frame. Finally, the position of the object is estimated accurately using the position model and the local patch model is updated. Experimental results indicate that the proposed algorithm not only can obtain a distinct improvement in coping with appearance change, local occlusion and background distraction, but also have high tracking efficiency.
- Visual tracking /
- Kernel ridge regression model /
- Fast Fourier Transform (FFT) /
- Patch exhaustive search /
- Position model

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参考文献(16)

Yang H X, Shao L, Zheng F, et al. Recent advances and trends in visual tracking: a review[J]. Neurocomputing, 2011, 74(18): 3823-3831.

Smeulders A W, Chu D M, Cucchiara R, et al. Visual tracking :an experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442-1468.

Wu Y, 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.

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

Collins R T. Mean-Shift blob tracking through scale space[C]. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Madison, United?States, 2003: 234-240.

Ning J F, Zhang L, et al. Robust mean shift tracking with corrected background-weighted histogram[J]. IET Computer Vision, 2012, 6(1): 62-69.

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.

Henriques J F, Caseiro R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]. Proceedings of European Conference on Computer Vision (ECCV), Florence,?Italy, 2012: 702-715.

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.

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.

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 W H, Chang F L, and Li T P. Adaptive fragments-based target tracking method fusing color histogram and SIFT features[J]. Journal of Electronics Information Technology, 2013, 35(4): 770-776.

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.

Yang F, Lu H C, and Chen Y W. Bag of feature tracking[C]. Proceedings of the International Conference on Pattern Recognition, Istanbul, Turkey, 2010: 153-156.

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

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

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