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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rius, I. Varona, J. Gonzalez, J. Villanueva, J.J. |
| Copyright Year | 2006 |
| Description | Author affiliation: Centre de Visio per Computador, Univ. Autonoma de Barcelona, Bellaterra (Rius, I.) |
| Abstract | Bayesian tracking implemented as a particle filter is one of the most used techniques for full-body human tracking. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions makes the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action model that guides the prediction step of the particle filter. In this manner, particles are propagated to locations in the search space with most a posteriori information. Hence, we sample from a smooth motion model only those postures which are feasible given a particular action. We show that this scheme improves the efficiency and accuracy of the overall tracking approach |
| Sponsorship | IEEE CPS |
| Starting Page | 472 |
| Ending Page | 475 |
| File Size | 228124 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769525210 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2006.206 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-08-20 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Humans Biological system modeling Particle tracking Particle filters Space exploration Legged locomotion Predictive models Filtering Aerospace industry |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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