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Color-based probabilistic tracking (2002)
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| Venue: | ECCV |
| Citations: | 352 - 6 self |
BibTeX
@ARTICLE{Perez02color-basedprobabilistic,
author = {P. Perez and C. Hue and J. Vermaak and M. Gangnet},
title = { Color-based probabilistic tracking},
journal = {ECCV},
year = {2002},
pages = {661--675}
}
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Abstract
Color-based trackers recently proposed in [3,4,5] have been proved robust and versatile for a modest computational cost. They are especially appealing for tracking tasks where the spatial structure of the tracked objects exhibits such a dramatic variability that trackers based on a space-dependent appearance reference would break down very fast. Trackers in [3,4,5] rely on the deterministic search of a window whose color content matches a reference histogram color model. Relying on the same principle of color histogram distance, but within a probabilistic framework, we introduce a new Monte Carlo tracking technique. The use of a particle filter allows us to better handle color clutter in the background, as well as complete occlusion of the tracked entities over a few frames. This probabilistic approach is very flexible and can be extended in a number of useful ways. In particular, we introduce the following ingredi-ents: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects.
Keyphrases
color-based probabilistic tracking rough spatial layout modest computational cost spatial structure space-dependent appearance reference probabilistic framework complete occlusion color clutter following ingredi-ents multi-part color modeling dramatic variability deterministic search particle filter color content probabilistic approach new monte carlo multiple object global histogram reference histogram color model tracked entity color histogram distance tracked object exhibit background color model color-based tracker useful way
