Title:
A Rao-Blackwellized Particle Filter for EigenTracking
A Rao-Blackwellized Particle Filter for EigenTracking
| dc.contributor.author | Khan, Zia | |
| dc.contributor.author | Balch, Tucker | |
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.date.accessioned | 2011-04-01T15:47:39Z | |
| dc.date.available | 2011-04-01T15:47:39Z | |
| dc.date.issued | 2004-06 | |
| dc.description | ©2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_US |
| dc.description | Presented at the 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27 June-2 July 2004, Washington, D.C. | |
| dc.description | DOI: 10.1109/CVPR.2004.1315271 | |
| dc.description.abstract | Subspace representations have been a popular way to model appearance in computer vision. In Jepson and Black’s influential paper on EigenTracking, they were successfully applied in tracking. For noisy targets, optimization-based algorithms (including EigenTracking) often fail catastrophically after losing track. Particle filters have recently emerged as a robust method for tracking in the presence of multi-modal distributions. To use subspace representations in a particle filter, the number of samples increases exponentially as the state vector includes the subspace coefficients. We introduce an efficient method for using subspace representations in a particle filter by applying Rao-Blackwellization to integrate out the subspace coefficients in the state vector. Fewer samples are needed since part of the posterior over the state vector is analytically calculated. We use probabilistic principal component analysis to obtain analytically tractable integrals. We show experimental results in a scenario in which we track a target in clutter. | en_US |
| dc.identifier.citation | Khan, Z., Balch, T., & Dellaert, F. (2004). "A Rao-Blackwellized Particle Filter for EigenTracking". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004), 27 June-2 July 2004, II:980-II:986. | en_US |
| dc.identifier.issn | 1063-6919 | |
| dc.identifier.uri | http://hdl.handle.net/1853/38361 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Analytically tractable integrals | en_US |
| dc.subject | Computer vision | en_US |
| dc.subject | Particle filter | en_US |
| dc.subject | Probabilistic principal component | en_US |
| dc.subject | Rao-Blackwellization | en_US |
| dc.subject | State vector | en_US |
| dc.subject | Subspace coefficients | en_US |
| dc.subject | Subspace representations | en_US |
| dc.title | A Rao-Blackwellized Particle Filter for EigenTracking | en_US |
| dc.type | Text | |
| dc.type.genre | Post-print | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | dac80074-d9d8-4358-b6eb-397d95bdc868 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |