PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using Particles to Track Varying Numbers of Interacting People
Kevin Smith, Daniel Gatica-Perez and Jean-Marc Odobez
In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2005, San Diego.

Abstract

In this paper, we present a Bayesian framework for the fully automatic tracking of a variable number of interacting targets using a fixed camera. This framework uses a joint multi-object state-space formulation and a trans-dimensional Markov Chain Monte Carlo (MCMC) particle filter to recursively estimate the multi-object configuration and efficiently search the state-space. We also define a global observation model comprised of color and binary measurements capable of discriminating between different numbers of objects in the scene. We present results which show that our method is capable of tracking varying numbers of people through several challenging real-world tracking situations such as full/partial occlusion and entering/leaving the scene.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:1283
Deposited By:Jean-Marc Odobez
Deposited On:28 November 2005