Title:
Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model
Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model
| dc.contributor.author | Balch, Tucker | |
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.author | Khan, Zia | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.date.accessioned | 2008-05-01T18:13:04Z | |
| dc.date.available | 2008-05-01T18:13:04Z | |
| dc.date.issued | 2003 | |
| dc.description.abstract | We describe a multiple hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets will actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/21300 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Domain | en_US |
| dc.subject | Insect behavior | en_US |
| dc.subject | Markov random field | en_US |
| dc.subject | Motion prior | en_US |
| dc.subject | Particle filter | en_US |
| dc.subject | Tracking application | en_US |
| dc.title | Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model | en_US |
| dc.type | Text | |
| dc.type.genre | Paper | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | Mobile Robot Laboratory | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | dac80074-d9d8-4358-b6eb-397d95bdc868 | |
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