Object Tracking Using Particle Filters in Moving Camera 


Vol. 37,  No. 5, pp. 375-387, May  2012


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  Abstract

This paper proposes a new real-time object tracking algorithm using particle filters with color and texture features in moving CCD camera images. If the user selects an initial object, this region is declared as a target particle and an initial state is modeled. Then, N particles are generated based on random distribution and CS-LBP (Centre Symmetric Local Binary Patterns) for texture model and weighted color distribution is modeled from each particle. For observation likelihoods estimation, Bhattacharyya distance between particles and their feature models are calculated and this observation likelihoods are used for weights of individual particles. After weights estimation, a new particle which has the maximum weight is selected and new particles are re-sampled using the maximum particle. For performance comparison, we tested a few combinations of features and particle filters. The proposed algorithm showed best object tracking performance when we used color and texture model simultaneously for likelihood estimation.

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  Cite this article

[IEEE Style]

B. Ko, J. Nam, J. Y. Kwak, "Object Tracking Using Particle Filters in Moving Camera," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 5, pp. 375-387, 2012. DOI: .

[ACM Style]

Byoung-Chul Ko, Jae-Yeal Nam, and Joon Young Kwak. 2012. Object Tracking Using Particle Filters in Moving Camera. The Journal of Korean Institute of Communications and Information Sciences, 37, 5, (2012), 375-387. DOI: .

[KICS Style]

Byoung-Chul Ko, Jae-Yeal Nam, Joon Young Kwak, "Object Tracking Using Particle Filters in Moving Camera," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 5, pp. 375-387, 5. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880