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Proceedings Paper

Using localized particle subset for target tracking in videos
Author(s): Lei Ma; Jennie Si; Glen P. Abousleman
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Paper Abstract

In this paper, a localized particle subset method is proposed to solve target tracking problem in a Bayesian inference framework. Instead of using all particles to estimated the posterior probability density function (pdf) of targets, a subset is used. This subset of particles is selected by estimated motion of the targets. The weights of particles are updated by the 3D Hausdroff distances between target appearance model and samples. The proposed method is highly efficient in utilizing the particles, which consequently results in reduction of samples utilized in the prediction and update processes. It is also able to alleviate the sample degeneracy and impoverishment problems in the sampling process. Experiments show that the computation complexity for localized particle subset tracker is reduce to a fraction of that of the Sequential Importance (SIS) tracker but with compatible performance.

Paper Details

Date Published: 17 May 2006
PDF: 12 pages
Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 623507 (17 May 2006); doi: 10.1117/12.665823
Show Author Affiliations
Lei Ma, Arizona State Univ. (United States)
Jennie Si, Arizona State Univ. (United States)
Glen P. Abousleman, General Dynamics C4 Systems (United States)


Published in SPIE Proceedings Vol. 6235:
Signal Processing, Sensor Fusion, and Target Recognition XV
Ivan Kadar, Editor(s)

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