Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2010.17B.1.037

2D Planar Object Tracking using Improved Chamfer Matching Likelihood  

Oh, Chi-Min (전남대학교 전자컴퓨터공학부)
Jeong, Mun-Ho (지능로봇연구센터(인지로봇연구단))
You, Bum-Jae (한국과학기술연구원(KIST) 지능로봇연구센터(인지로봇연구단))
Lee, Chil-Woo (전남대학교 전자컴퓨터공학부)
Publication Information
The KIPS Transactions:PartB / v.17B, no.1, 2010 , pp. 37-46 More about this Journal
Abstract
In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.
Keywords
Model based Object Tracking; Particle Filter; Distance Transform; Chamfer Matching;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. Zhou, Y. Yuan, C. Shi, “Object Tracking using SIFT Features and Mean Shift,” Computer Vision and Image Understanding, Vol.113, No3, pp.345-352, Mar., 2009.   DOI   ScienceOn
2 M. Isard and J. MacCormick “BraMBLe: A Bayesian Multiple-Blob Tracker,” Proceedings. International Conference on Computer Vision, Vol.2, pp.34-41, Jul., 2001.   DOI
3 E. Polat, M. Yeasin, R. Sharma, “A 2D/3D Model-based Object Tracking Framework,” Pattern Recognition, Vol.33, No.9, pp.2127-2141, Sep., 2003.
4 A. L. Barker, D. E. Brown, W. N. Martin, “Bayesian Estimation and the Kalman Filter,” Computer & Mathematics with Applications, Vol.30, No.10, pp.55-77, Nov., 1995.   DOI   ScienceOn
5 S. Arulampalam, S. Maskell, N. Gordon, T. Clapp, “A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking,” IEEE Transaction on Signal Processing, Vol.50, No.2, pp.174-188, Feb., 2002.   DOI   ScienceOn
6 A. D. Jepson, D. J. Feet and T. F. El-Maraghi, “Robust Online Appearance Models for Visual Tracking,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.25, No.10, pp.1296-1311. Oct., 2003.   DOI   ScienceOn
7 G. R. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface,” Intel Technology Journal, May(Q2), 1998.
8 H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, H. C. Wolf, “Parametric Correspondence and Chamfer Matching: Two New Technique for Image Matching,” in Proc. 5th International Joint Conference on Artificial Intelligence, pp.1175-1177, Aug., 1997.
9 D. Koller, K. Daniilidis, H. -H. Nagel. “Model-based Object Tacking in Monocular Image Sequences of Road Traffic Scenes,” International Journal of Computer Vision, Vol10, No.3, pp.257-281, Jun., 1993.   DOI
10 X. Zhang, C. Li, X. Tong, W. Hu, S. Maybank, Y. Zhang, “Efficient Human Pose Estimation via Parsing a Tree Structure based Human Model,” IEEE International Conference on Computer Vision, Vol.2, pp.1349-1356, Sep., 2009.
11 Md. Z. Islam, C. M. Oh, J. S. Yang, and C. W. Lee, “DT Template based Moving Object Tracking with Shape Information by Particle Filter,” IEEE Cybernetic Intelligent Systems, Vol.1, pp.127-132, Sep., 2008.
12 E. Rosten and T. Drummond “Machine learning for higspeed corner detection,” European Conference on Computer Vision, Vol.1, pp.430-443, May, 2006.   DOI   ScienceOn
13 Y. Wu, Lin. J. T. S. Huang “Analyzing and Capturing Articulated Hand Motion in Image Sequences,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.27, No.12, pp.1910-1922. Dec., 2005.   DOI   ScienceOn
14 S. K. Weng, C. M. Kuo, S. K. Tu, “Video Object Tracking using Adaptive Kalman Filter,” Journal of Visual Communication and Image Representation, Vol.17, No.6, pp.1190-1208, Dec., 2006.   DOI   ScienceOn