Fast fragment based tracking using adaptive search

WU Ben-tao; WU Min-yuan; ZENG Lin

doi:10.3788/OPE.20111903.0703

您当前的位置：

首页 >

文章列表页 >

Fast fragment based tracking using adaptive search

Article | 更新时间：2020-08-12

- Fast fragment based tracking using adaptive search
- Optics and Precision Engineering Vol. 19, Issue 3, Pages: 703-708(2011)
- 作者机构：
  
  武汉大学电子信息学院,湖北武汉 430079
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111903.0703
  CLC： TP391
- Received：02 April 2010，
  
  Revised：23 July 2010，
  
  Published Online：22 March 2011，
  
  Published：22 March 2011
- 稿件说明：
移动端阅览
吴本涛, 吴敏渊, 曾霖. 自适应搜索的快速分块跟踪[J]. 光学精密工程, 2011,19(3): 703-708

WU Ben-tao, WU Min-yuan, ZENG Lin. Fast fragment based tracking using adaptive search[J]. Editorial Office of Optics and Precision Engineering, 2011,19(3): 703-708
吴本涛, 吴敏渊, 曾霖. 自适应搜索的快速分块跟踪[J]. 光学精密工程, 2011,19(3): 703-708 DOI： 10.3788/OPE.20111903.0703.

WU Ben-tao, WU Min-yuan, ZENG Lin. Fast fragment based tracking using adaptive search[J]. Editorial Office of Optics and Precision Engineering, 2011,19(3): 703-708 DOI： 10.3788/OPE.20111903.0703.

摘要

针对传统的分块跟踪算法计算量大

难以实时地对运动目标进行跟踪这一问题

提出了一种改进的分块跟踪算法。首先

为降低背景噪声对跟踪性能产生的不利影响

提出的算法对目标所在矩形窗口进行了更细致的划分;然后

根据目标运动信息确定搜索范围和搜索中心

采用分层次的自适应搜索算法

在每一层采用不同的搜索策略逐步逼近与目标模板最相似的位置

避免算法将时间过多地浪费在无效位置的运算上;最后

给出了改进算法在DSP上的实现和优化方法。实验结果显示

该改进算法能够在DM642上以30 frame/s的速度处理768 pixel576 pixel的图像

与传统的分块跟踪算法相比

提高了跟踪精度

运算时间减小了约47.5%。该改进算法较好地解决了传统分块算法的缺陷

实现了在嵌入式系统上对运动目标的实时跟踪。

Abstract

For the large computation and the real-time tracking to be hard to achieve by traditional fragment based tracking algorithm

an improved fragment based algorithm was proposed. Firstly

in order to reduce the negative effect yielded by the background noise

the region of the tracked object was divided into more fragments. Then

the position and the range of the search region were identified according to the movement information of the object. By utilizing a fast hierarchical adaptive search approach which adopts different search patterns in different steps

most of the calculations for invalid positions were skipped

and the coordinate where the candidate was most similar with the object template was obtained quickly. Moreover

the improved algorithm was implemented and optimized on a DSP. Experimental results indicate that the improved algorithm can process the image of 768 pixel576 pixel on DM642 at a processing speed of 30 frame/s. Compared with the traditional fragment based tracking algorithm

it shows a more precise tracking and saves the processing time about 47.5%. These results show that the improved algorithm overcome the shortcomes from traditional fragment based tracking algorithms

and can achieve a real-time tracking with better tracking performance.

关键词

Keywords

references

施家栋,王建中. 人体运动检测的光流与肤色融合方法 [J]. 北京理工大学学报,2009,29(4):347-350. SHI J D, WANG J ZH. Detection of human motion using a fusion method based on optical flow and skin color [J]. Journal of Beijing Institute of Technology (Natural Science Edition), 2009,29(4):347-350. (in Chinese)[2] 颜佳,吴敏渊,陈淑珍,等. 跟踪窗口自适应的Mean Shift跟踪 [J]. 光学精密工程, 2009,17(10):2606-2611. YAN J, WU M Y, CHEN SH ZH, et al.. Mean Shift tracking with adaptive tracking window [J]. Opt. Precision Eng., 2009,17(10):2606-2611. (in Chinese)[3] 吴川,杨冬,郝志成. 基于粒子滤波的彩色图像跟踪 [J]. 光学精密工程, 2009,17(10):2542-2547. WU CH, YANG D, HAO ZH CH. Color image tracking algorithm based on particle filter [J]. Opt. Precision Eng., 2009,17(10):2542-2547. (in Chinese)[4] 魏坤,赵永强,潘泉,等. 基于均值漂移和粒子滤波的红外目标跟踪 [J]. 光电子激光, 2008,19(2):213-217. WEI K, ZHAO Y Q, PAN Q, et al.. IR target tracking based on mean shift and particle filter [J]. Journal of OptoelectronicsLaser, 2008,19(2):213-217. (in Chinese)[5] 张恒,李立春,于起峰. 尺度方向自适应Mean Shift跟踪算法 [J]. 光学精密工程,2008,16(6):1133-1139. ZHANG H, LI L CH, YU Q F. Scale and direction adaptive Mean Shift tracking algorithm [J]. Opt. Precision Eng., 2008,16(6):1133-1139. (in Chinese)[6] ADAM A, RIVLIN E, SHIMSHONI I. Robust fragments-based tracking using the integral histogram . Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY. CVPR, 2006:798-805.[7] LAO Y W, ZHU J D, ZHENG Y F. Sequential particle generation for visual tracking [J]. Circuits and Systems for Video Technology,2009,19(9):1365-1378.[8] SRIKRISHNAN V, NAGARAJ T, CHAUDHURI S. Fragment based tracking for scale and orientation adaptation . Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, ICVGIP, 2008,19:328-335. [9] WANG F L, YU SH Y, YANG J. A novel fragments-based tracking algorithm using mean shift . 10th International Conference on Control, Automation, Robotics and Vision, Hanoi, ICARCV, 2008:694-698. [10] LIN CH CH, LIN Y, HSIEH H J. Multi-direction search algorithm for block motion estimation in H.264/AVC [J]. Image Processing, IET,2009,3(2):88-99. [11] ZHU S, MA K K. A new diamond search algorithm for fast block matching motion estimation [J]. IEEE Trans. Image Processing, 2000,9(2):287-290. [12] http://www.cvg.rdg.ac.uk/PETS2009/.

Views

604

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰