PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dynamic Partitioned Sampling For Tracking With Discriminative Features
Stefan Duffner, Jean-Marc Odobez and elisa ricci
In: British Machine Vision Conference (BMVC), Londion, England(2009).

Abstract

We present a multi-cue fusion method for tracking with particle filters which relies on a novel hierarchical sampling strategy. Similarly to previous works, it tackles the problem of tracking in a relatively high-dimensional state space by dividing such a space into partitions, each one corresponding to a single cue, and sampling from them in a hierarchical manner. However, unlike other approaches, the order of partitions is not fixed a priori but changes dynamically depending on the reliability of each cue, i.e. more reliable cues are sampled first. We call this approach Dynamic Partitioned Sampling (DPS). The reliability of each cue is measured in terms of its ability to discriminate the object with respect to the background, where the background is not described by a fixed model or by random patches but is represented by a set of informative ”background particles“ which are tracked in order to be as similar as possible to the object. The effectiveness of this general framework is demonstrated on the specific problem of head tracking with three different cues: colour, edge and contours. Experimental results prove the robustness of our algorithm in several challenging video sequences.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6771
Deposited By:Jean-Marc Odobez
Deposited On:08 March 2010