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http://dx.doi.org/10.6109/jkiice.2011.15.2.364

A Novel Method for Moving Object Tracking using Covariance Matrix and Riemannian Metric  

Lee, Geum-Boon (조선대학교 컴퓨터공학부)
Cho, Beom-Joon (조선대학교 컴퓨터공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.15, no.2, 2011 , pp. 364-370 More about this Journal
Abstract
This paper propose a novel method for tracking moving object based on covariance matrix and Riemannian Manifolds. With image backgrounds continuously changed, we use the covariance matrices to extract features for tracking nonrigid object undergoing transformation and deformation. The covariance matrix can make fusion of different types of features and has its small dimension, therefore we enable to handle the spatial and statistical properties as well as the component correlation. The proposed method can estimate the position of the moving object by employing the covariance matrix of object region as a feature vector and comparing the candidate regions. Rimannian Geometry is efficiently adapted to object deformation and change of shape and improve the accuracy by using geodesic distance to predict the estimated position with the minimum distance. The experimental results have shown that the proposed method correctly tracked the moving object.
Keywords
Moving object tracking; Covariance matrix; Riemannian manifolds; Geodesic distance;
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  • Reference
1 X. Pennec, P. Fillard, and N. Ayache, "A riemannian framework for tensor computing," In International Journal of Computer Vision, 2006.
2 F. Porikli, O. Tuzel, and P. Meer, "Covariance Tracking using Model Update Based on Means on Riemannian Manifolds," Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 728-735, 2006.
3 W. Forstner and B. Moonen, A metric for covariance matrices, Technical report, Dept. of Geodesy and Geoinformatics, Stuttgart University, 1999.
4 L. Lu, X. Dai, and gd Hager, "Efficient particle filtering using RANSAC with application to 3D face tracking," Image Vision Computing, vol. 24, no. 6, pp. 581-592, 2006.   DOI   ScienceOn
5 A. D. Jepson, D. J. Fleet, and T. F. EI-Maraghi, "Robust Online Appearance Models for Visual Tracking," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, 2003.   DOI   ScienceOn
6 O. Tuzel, F. Porikli, and P. Meer, "Region covariance: A fast descriptor for detection and classification," In Proc. 9th European Conf. on computer Vision, vol. 2, pp. 589-600, 2006.
7 S. J. Julier and J. K. Uhlmann, "A New Extension of the Kalman Filter to Nonlinear Systems," SPIE AeroSense Symposium, 1997.
8 D. Comaniciu, V. Ramesh, and P. Neer, "Real-time tracking of non-rigid objects using mean shift," IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 142-149, 2000.
9 Gary R. Bradski, "Computer Vision Face Tracking for Use in a Perceptual User Interface," Intel Technology Journal, no. Q2, 1998.