Study on Particle Filter Object Tracking Based on Weighted Fusion

Zhi Qiang Wen; Yan Hui Zhu; Zhao Yi Peng

doi:10.4028/www.scientific.net/AMR.403-408.3049

Paper Titles

Study of High Precision Frequency Measurement Method of Power System Based on DSP
p.3030

A Rapid Behavior Simulation Approach for Fishes
p.3035

Research on Technology of Semantic Fusion in Geospatial Information Based on Ontology
p.3039

The Design and Simulation of Vehicle Electronic Control Network Based on CAN/LIN Bus
p.3044

Study on Particle Filter Object Tracking Based on Weighted Fusion
p.3049

Digital Watermarking Based on Chaotic Encryption and Wavelet Transformation
p.3054

The Singular Function Method Based on the Moving Load Deformation of Suspension Bridge
p.3059

Image Enhancement Based on Space Domain with MATLAB
p.3063

An Integration Framework of Product Design Knowledge Oriented to Collaborative Design
p.3067

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Study on Particle Filter Object Tracking Based on...

Study on Particle Filter Object Tracking Based on Weighted Fusion

Abstract:

For the lack of self-adaptivity to environments, a measurement model based on weighted information fusion is presented in particle filter. Combined with color information and movement information of object, color histogram and motion Information histogram are built respectively, and then present a weighted linear model as the measurement model. The center-around method is adopted to compute the weight in the linear model. Lastly, a mass of experiments show the presented method be effective.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

3049-3053

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.3049

Citation:

Cite this paper

Online since:

November 2011

Authors:

Zhi Qiang Wen, Yan Hui Zhu, Zhao Yi Peng

Keywords:

Measurement Model, Object Tracking, Particle Filter (PF)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] S. K. Zhou, R. Chellappa, et al. Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters, IEEE Trans. on Image Processing, vol. 11, no. 13, pp.1491-1506, (2004).

DOI: 10.1109/tip.2004.836152

Google Scholar

[2] Y. Cha, D. Bi. An Adaptive Particle Filter for Moving Objects Tracking, J. of Electronics & Information Technology, vol. 1, no. 29(2007), pp.93-95.

Google Scholar

[3] S. Park, Y. Kim, M. Lim. Novel Adaptive Particle Filter Using Adjusted Variance and Its Application, Int. J. of Control, Automation, and Systems, vol. 4, no. 8(2010), pp.801-807.

DOI: 10.1007/s12555-010-0412-4

Google Scholar

[4] G. ZHANG, Y. CHENG, F. YANG, et al. Design of an Adaptive Particle Filter Based on Variance Reduction, ACTA AUTOMATICA SINICA, vol. 7, no. 36(2010), pp.1020-1024.

DOI: 10.3724/sp.j.1004.2010.01020

Google Scholar

[5] P. Brasnett, L. Mihaylova, et al. Sequential Monte Carlo tracking by fusing multiple cues in video sequences, Image and Vision Computing, vol. 8, no. 25(2007), pp.1217-1227.

DOI: 10.1016/j.imavis.2006.07.017

Google Scholar

[6] E. Maggio, F. Smeraldi, et al. Adaptive multi-feature tracking in a particle filtering framework, IEEE Trans. on Circuits and Systems for Video Technology, vol. 10, no. 17(2007), pp.1348-1359.

DOI: 10.1109/tcsvt.2007.903781

Google Scholar

[7] D. Comaniciu, V. Ramesh, et al. Kernel-based object tracking. TPAMI, vol. 25, no. 5(2003), pp.564-577.

Google Scholar