Color-Based Probabilistic Tracking

  • Authors:
  • Patrick Pérez;Carine Hue;Jaco Vermaak;Michel Gangnet

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
  • Year:
  • 2002

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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 ingredients: 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.