Using Particles to Track Varying Numbers of Interacting People

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 transdimensional 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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Kevin Smith, Daniel Gatica-Perez, Jean-Marc Odobez
• Keywords: Computer Vision, CVPR 2005, Full/partial Occlusion, Multi-object Configuration, Multi-object State-space Formulation, Real-world Tracking Situations, Transdimensional Markov Chain