Using Particles to Track Varying Numbers of Interacting People

Authors:
Kevin Smith;Daniel Gatica-Perez;Jean-Marc Odobez
Affiliations:
IDIAP Research Institute;IDIAP Research Institute;IDIAP Research Institute
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Year:
2005

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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 alsodefine 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 andentering/leaving the scene.