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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Smith, K. Gatica-Perez, D. Odobez, J.-M. |
| Copyright Year | 2005 |
| Description | Author affiliation: IDIAP Res. Inst., Martigny, Switzerland (Smith, K.; Gatica-Perez, D.; Odobez, J.-M.) |
| 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 estimates 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. |
| Sponsorship | IEEE Comput. Soc |
| Starting Page | 962 |
| Ending Page | 969 |
| File Size | 428699 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769523722 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2005.361 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle tracking Sampling methods Bayesian methods Layout Target tracking Particle filters State-space methods Cameras Monte Carlo methods Recursive estimation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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