Tracking with a new distribution metric in a particle filtering framework

Romeil Sandhu; Tryphon Georgiou; Allen Tannenbaum

doi:10.1117/12.768592

26 February 2008 Tracking with a new distribution metric in a particle filtering framework

Romeil Sandhu, Tryphon Georgiou, Allen Tannenbaum

Proceedings Volume 6813, Image Processing: Machine Vision Applications; 68130N (2008) https://doi.org/10.1117/12.768592
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Romeil Sandhu, Tryphon Georgiou, and Allen Tannenbaum "Tracking with a new distribution metric in a particle filtering framework", Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130N (26 February 2008); https://doi.org/10.1117/12.768592

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