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Particle Filtering based Object Tracking Method using Feedback and Tracking Box Correction  

Ahn, Jung-Ho (강남대학교 컴퓨터미디어정보공학부)
Publication Information
Journal of Satellite, Information and Communications / v.8, no.1, 2013 , pp. 77-82 More about this Journal
Abstract
The object tracking method using particle filtering has been proved successful since it is based on the Monte Carlo simulation to estimate the posterior distribution of the state vector that is nonlinear and non-Gaussian in the real-world situation. In this paper, we present two nobel methods that can improve the performance of the object tracking algorithm based on the particle filtering. First one is the feedback method that replace the low-weighted tracking sample by the estimated state vector in the previous frame. The second one is an tracking box correction method to find an confidence interval of back projection probability on the estimated candidate object area. An sample propagation equation is also presented, which is obtained by experiments. We designed well-organized test data set which reflects various challenging circumstances, and, by using it, experimental results proved that the proposed methods improves the traditional particle filter based object tracking method.
Keywords
particle filtering; feedback; box correction; back projection; Propagation Equation; color histogram;
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1 안정호, 강봉, 황인욱, "추적박스 보정을 이용한 향상된 Particle Filter 객체 추적 방법론", 멀티미디어학회 추계학술발 표대회 논문집, 15권 2호, pp. 355-358, 2012.
2 A. D. Bimbo and F. Dini, "Particle filter-based visual tracking with a first order dynamic model and uncertainty adaptation", Computer Vision and Image Understanding, vol. 115, no. 6, pp. 771-786, 2011.   DOI   ScienceOn
3 G.R. Bradski. "Computer vision face tracking as a component of a perceptual user interface", In Workshop on Applications of Computer Vision, pp. 214-219, 1998.
4 D. Comaniciu, V. Ramesh and P. Meer, "Real-Time Tracking of Non Rigid Objects using Mean Shift", International Conference on Computer Vision and Pattern Recognition, pp. 70-73, 2000.
5 M. Isard and A. Blake, "CONDENSATION - Conditional Density Propagation for Visual Tracking", International Journal on Computer Vision, Vol. 1, No. 29, pp. 5-28, 1998.
6 T. Kailath, "The Divergence and Bhattacharyya Distance Measures in Signal Selection", IEEE Transactions on Communication Technology COM, vol. 15, no.1, pp.52-60, 1967.   DOI
7 K. Nummiaro, E. Koller-Meier and L. V. Gool, "An Adaptive Color-Based Particle Filter" Image and Vision Computing, Vol. 21, pp. 99-110, 2002.
8 I. T. Phillips and A. K. Chhabra, "Empirical performance evaluation of graphics recognition systems", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 849-870, 1999.   DOI   ScienceOn
9 Y. Bar-Shalon and T. Fortmann, Tracking and data association, Academic Press, 1988.
10 G. Welch and G. Bishop, "An Introduction to Kalman filter", Technical Report (TR 95-041), University of North Carolina at Chaple Hill, 2004.
11 H. Yang, L. Shao, F. Zheng, L. Wang and Z. Song, "Recent advances and trends in visual tracking: A review", Neurocomputing, vol.74, pp. 3823-3831, 2011.   DOI   ScienceOn
12 A. Yilmaz, O. Javed and M. Shah, "Object tracking: A survey", ACM Journal of Computing Surveys, vol. 38, no. 4, 2006.