An Adaptive Particle Filter for Moving Objects Tracking

Zha Yu-fei; Bi Du-yan

doi:10.3724/SP.J.1146.2005.00492

Volume 29 Issue 1

Jan. 2011

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(1): 92-95

Zha Yu-fei, Bi Du-yan. An Adaptive Particle Filter for Moving Objects Tracking[J]. Journal of Electronics & Information Technology, 2007, 29(1): 92-95. doi: 10.3724/SP.J.1146.2005.00492

Citation:

Zha Yu-fei, Bi Du-yan. An Adaptive Particle Filter for Moving Objects Tracking[J]. Journal of Electronics & Information Technology, 2007, 29(1): 92-95. doi: 10.3724/SP.J.1146.2005.00492

Zha Yu-fei, Bi Du-yan. An Adaptive Particle Filter for Moving Objects Tracking[J]. Journal of Electronics & Information Technology, 2007, 29(1): 92-95. doi: 10.3724/SP.J.1146.2005.00492

Citation:

Zha Yu-fei, Bi Du-yan. An Adaptive Particle Filter for Moving Objects Tracking[J]. Journal of Electronics & Information Technology, 2007, 29(1): 92-95. doi: 10.3724/SP.J.1146.2005.00492

PDF( 688 KB)

An Adaptive Particle Filter for Moving Objects Tracking

doi: 10.3724/SP.J.1146.2005.00492

Received Date: 2005-04-30
Rev Recd Date: 2005-09-09
Publish Date: 2007-01-19

Abstract

Abstract

In this paper, an adaptive particle filter for moving objects tracking is proposed. Mean shift is optimization algorithm based on gradient descended, which tracks moving targets through iterations. Particle filter is a robust method of tracking in non-Gauss and non-linear case. Firstly, a target model based on statistical histogram is proposed, which improves the classical histogram. Then Mean Shift algorithm and particle filter are integrated novelly through the statistical histogram target model. The parameters are modified according to the processing of tracking, so the effects caused by changed light or occlusion can be overcome. Experiments show that the method proposed by this paper can track moving target more powerful than Mean Shift tracked. Otherwise, even in complicated case, this method can still efficiently work.

FullText(HTML)

References(1)

References

[1] Paragios N and Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. on Pattern Anal. Mach. Intell., 2000, 3(22): 262－280. [2] Arulampalam M, Maskell S, Gordon N, and Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Trans. on Singal Processing.2002, 50(2):174- [3] Doucet A, Gordon N, and Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems[J].IEEE Trans. on Signal Processing.2001, 49(3):613- [4] Comaniciu D, Ramesh V, and Meer P. Real-time tracking of non-rigid objects using mean shift. IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina. 2000, II: 142－149. [5] Kailath T. The divergence and Bhattacharyya dstance measures in signal selection. IEEE Trans. on Commun. Technol., 1967, COM-15: 52－60. [6] Perez P, Hue C, Vermaak J, and Gangnet M. Color-based probabilistic tracking. European Conference on Computer Vision. Copenhagen, Denmark. 2002, 1: 661－675. [7] Nummiaro K.[J].Koller-Meier E, and Van Gool L. Object tracking with an adaptive color-based particle filter. First International Workshop on Generative-Model-Based Vision, in conjunction with ECCV02.Copenhagen, Denmark.2002,:-

Relative Articles

Supplements(0)

Cited By

Proportional views