[1]WANG Jie,JIANG Mingmin,HUA Xiaohui,et al.Binocular object tracking method using projection histogram matching[J].CAAI Transactions on Intelligent Systems,2015,10(5):775-782.[doi:10.11992/tis.201410009]
Copy

Binocular object tracking method using projection histogram matching

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 5 Page number: 775-782 Column: 学术论文—机器感知与模式识别 Public date: 2015-10-25
Title:
Binocular object tracking method using projection histogram matching
Author(s):
WANG Jie1; 2;  JIANG Mingmin1;  HUA Xiaohui3; 4;  LU Shouyin3; 5;  LI Jinping1; 2
1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China;
2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China;
3. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China;
4. Shandong Province Key Laboratory of Intelligent Building, Shandong Jianzhu University, Jinan 250101, China;
5. Shandong Electric Power Research Institute, Jinan 250002, China
Keywords:
projection histogram; object tracking; binocular vision; epipolar geometry; template matching
CLC:
TP391
DOI:
10.11992/tis.201410009
Abstract:
An effective object location and tracking method is proposed for a robot manipulator’s binocular visual servo system by utilizing projection histogram matching and the epipolar geometric constraint. The object is selected manually in the two vision systems, then the horizontal and vertical projection histograms, in multiple color spaces of the selected object, are extracted as matching templates for these two vision systems. The object in one vision system can be tracked by making use of the principle of moving continuity and the matching templates of the projection histograms in multiple color spaces. In the other vision system, the object is located roughly based on epipolar geometric constraint, and then it can be tracked accurately using the same method which is used in the first vision. The proposed method describes the object’s structural information by using horizontal and vertical projection histograms, and realizes the function of object tracking and registration in the binocular visual system, which is helpful in the precise location and visual measurement of the tracked object. The experiment results indicate that this method can realize the binocular object tracking with high efficiency and strong robustness.
References:
[1] HOSEINNEZHAD R, VO B N, VO B T. Visual tracking in background subtracted image sequences via multi-Bernoulli filtering[J]. IEEE Transactions on Signal Processing,2013, 61(2):392-397.
[2] 王乐东, 王江安, 康圣, 等. 相邻帧间匹配的迎头点目标跟踪算法[J]. 光学学报, 2009, 29(11):3004-3009. WANG Ledong, WANG Jiang’an, KANG Sheng, et al. An algorithm of adjacent frame matching used for head-on point targets tracking[J]. Acta Optica Sinica, 2009, 29(11):3004-3009.
[3] ALLEN J G, XU R Y D, JIN J S. Object tracking using camshift algorithm and multiple quantized feature spaces[C]//Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing. Darlinghurst, Australia, 2004:3-7.
[4] ZHANG Shengping, YAO Hongxun, SUN Xin, et al. Sparse coding based visual tracking:review and experimental comparison[J]. Pattern Recognition, 2013, 46(7):1772-1788.
[5] TSECHPENAKIS G, RAPANTZIKOS K, TSAPATSOULIS N, et al. A snake model for object tracking in natural sequences[J]. Signal Processing:Image Communication, 2004, 19(3):219-238.
[6] KOSCHAN A, KANG S, PAIK J, et al. Color active shape models for tracking non-rigid objects[J]. Pattern Recognition Letters, 2003, 24(11):1751-1765.
[7] 邱雪娜, 刘士荣. 基于序贯检测机制的双目视觉运动目标跟踪与定位方法[J]. 机器人, 2011, 33(2):181-190. QIU Xuena, LIU Shirong. Moving objects tracking and localization with binocular vision based on sequential detection mechanism[J]. Robot, 2011, 33(2):181-190.
[8] 王丽佳, 贾松敏, 李秀智, 等. 多特征提取的双目机器人目标跟踪[J]. 控制与决策, 2013, 28(10):1568-1572. WANG Lijia, JIA Songmin, LI Xiuzhi, et al. Person following of binocular robot by extracting multiple features[J]. Control and Decision, 2013, 28(10):1568-1572.
[9] ZOIDI O, NIKOLAIDIS N, TEFAS A, et al. Stereo object tracking with fusion of texture, color and disparity information[J]. Signal Processing:Image Communication, 2014, 29(5):573-589.
[10] 解洪文, 王宏伦. 基于双目视觉的自动空中加油近距导航方法[J]. 北京航空航天大学学报, 2011, 37(2):206-209. XIE Hongwen, WANG Honglun. Binocular vision-based short-range navigation method for autonomous aerial refueling[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(2):206-209.
[11] 祝琨, 杨唐文, 阮秋琦, 等. 基于双目视觉的运动物体实时跟踪与测距[J]. 机器人, 2009, 31(4):327-334. ZHU Kun, YANG Tangwen, RUAN Qiuqi, et al. Real-time tracking and measuring of moving objects based on binocular vision[J]. Robot, 2009, 31(4):327-334.
[12] MUFFERT M, PFEIFFER D, FRANKE U. A stereo-vision based object tracking approach at roundabouts[J]. IEEE Intelligent Transportation Systems Magazine, 2013, 5(2):22-32.
[13] CAI Ling, HE Lei, XU Yiren, et al. Multi-object detection and tracking by stereo vision[J]. Pattern Recognition, 2010, 43(12):4028-4041.
[14] LI Jinping, QIN Min, XIA Yingjie, et al. Remarks on a novel statistical histogram-average scene cumulative histogram[C]//2012 IEEE International Conference on Granular Computing. Hangzhou, China, 2012:253-257.
[15] 张广军. 视觉测量[M]. 北京:科学出版社, 2008:134-174. ZHANG Guangjun. Vision measurement[M]. Beijing:Science Press, 2008:134-174.
[16] 吴成茂. 直方图均衡化的数学模型研究[J]. 电子学报, 2013, 41(3):598-602. WU Chenmao. Studies on mathematical model of histogram equalization[J]. Acta Electronica Sinica, 2013, 41(3):598-602.
[17] 余旺盛, 侯志强, 田孝华. 基于旋转不变直方图的快速匹配穷搜索[J]. 电子学报, 2012, 40(11):2177-2182. YU Wangsheng, HOU Zhiqiang, TIAN Xiaohua. Swift matching exhaustive search based on rotation invariant histogram[J]. Acta Electronica Sinica, 2012, 40(11):2177-2182.
[18] BRADSKI G, KAEHLER A. Learning OpenCV:Computer vision with the OpenCV library[M].[S.l.]:O’Reilly Media, Inc., 2008:405-458, 193-219.
Similar References:

Memo

-

Last Update: 2015-11-16

Copyright © CAAI Transactions on Intelligent Systems