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Proceedings Paper

Tracking with a new distribution metric in a particle filtering framework
Author(s): Romeil Sandhu; Tryphon Georgiou; Allen Tannenbaum
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Paper Abstract

Tracking involves estimating not only the global motion but also local perturbations or deformations corresponding to a specified object of interest. From this, motion can be decoupled into a finite dimensional state space (the global motion) and the more interesting infinite dimensional state space (deformations). Recently, the incorporation of the particle filter with geometric active contours which use first and second moments has shown robust tracking results. By generalizing the statistical inference to entire probability distributions, we introduce a new distribution metric for tracking that is naturally able to better model the target. Also, due to the multiple hypothesis nature of particle filtering, it can be readily seen that if the background resembles the foreground, then one might lose track. Even though this can be described as a finite dimensional problem where global motion can be modeled and learned online through a filtering process, we approach this task by incorporating a separate energy term in the deformable model that penalizes large centroid displacements. Robust results are obtained and demonstrated on several surveillance sequences.

Paper Details

Date Published: 26 February 2008
PDF: 9 pages
Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130N (26 February 2008); doi: 10.1117/12.768592
Show Author Affiliations
Romeil Sandhu, Georgia Institute of Technology (United States)
Tryphon Georgiou, Univ. of Minnesota (United States)
Allen Tannenbaum, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6813:
Image Processing: Machine Vision Applications
Kurt S. Niel; David Fofi, Editor(s)

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