Object Tracking and Positioning Based on Stereo Vision

Article Preview

Abstract:

The goal of this paper is to present a method for object tracking and positioning based on stereo vision in real time. The method effectively combined stereo matching algorithm with object tracking algorithm, and calculated the spatial location information of the object by using binocular stereo vision while the object is being tracked. The stereo matching used dynamic programming, image pyramids and control points modification algorithm, and the object tracking mainly utilized CamShift algorithm in this paper. The experimental results have confirmed that the proposed method realized real-time tracking for moving object, accurate calculating for the object three-dimensional coordinates, which meet the applied needs of servo follow-up system.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

313-317

Citation:

Online since:

February 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Nalpantidis L, Georgios C S, and Antonios G: International Journal of Optomechatronics. Vol 2(2008), pp.435-462.

Google Scholar

[2] J A. Yilmaz, O. Javed, and M. Shah: ACM Computing Surveys. Vol 38(2006), pp.13-59.

Google Scholar

[3] E. David, B. Erich, K. Daniel, and S. Anselm: Fast and Robust Camshift Tracking. IEEE Transaction. Vol 8(2010), pp.1-8.

Google Scholar

[4] S. Franois, B. and R.J. Alexandre: CamShift Tracker Design Experiments with Intel OpenCV and Sai. IMSC. Vol 11(2004), pp.1-11.

Google Scholar

[5] Kun Zhu, TangWen Yang, QiuQi Ruan et al: Robot. Vol 31(2009), pp.327-334. In Chinese.

Google Scholar

[6] Juan Zhang, JianShou Pan: Computer Engineering and Application. Vol 45(2009), pp.191-194. In Chinese.

Google Scholar

[7] Cheng Y: Mean shift, mode seeking, and clustering. IEEE Translation on Pattern Analysis and Machine Intelligence. Vol 17(1995), pp.790-799.

DOI: 10.1109/34.400568

Google Scholar

[8] L. Ido, and L. Michael, and R. Ehud: Computer Vision and Image Understanding. Vol 9(2010), pp.400-408.

Google Scholar

[9] R.I. Hartley: International Journal of Computer Vision. Vol 35(1999), pp.115-127.

Google Scholar

[10] A. Fusiello, E. Trucco, A. Verri: Machine Vision and Applications. Vol 12(2000), pp.16-22.

Google Scholar

[11] M. Gong, Y.H. Yang: Fast Stereo matching using reliability-based dynamic programming and consistency constraints. Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV'03), (2003).

DOI: 10.1109/iccv.2003.1238404

Google Scholar

[12] Bobick A F, Intille S S: Large occlusion stereo. International Journal of Computer Vision. Vol 33(1999), pp.181-200.

Google Scholar

[13] E. H. Adelson, C. H. Anderson,J. R. Bergen,P. J. Burt,J. M. Ogden: RCA Engineer. Vol 29(1984), pp.33-41.

Google Scholar

[14] Zhang Z Y: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol22(2000), pp.1330-1334.

DOI: 10.1109/34.888718

Google Scholar

[15] Zhang Z Y: Camera calibration with one-dimensional objects. Transactions on Pattern Analysis and Machine Intelligence. Vol 26(2004), pp.892-899.

DOI: 10.1109/tpami.2004.21

Google Scholar