A bayesian formulation for multi-bernoulli random finite sets in multi-target tracking
dc.contributor.author | Roulston, Yasmin | |
dc.contributor.author | Peursum, Patrick | |
dc.contributor.editor | Not known | |
dc.date.accessioned | 2017-01-30T11:14:44Z | |
dc.date.available | 2017-01-30T11:14:44Z | |
dc.date.created | 2013-03-24T20:00:32Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Roulston, Yasmin and Peursum, Patrick. 2012. A bayesian formulation for multi-bernoulli random finite sets in multi-target tracking, in International Conference on Digital Image Computing Techniques and Applications (DICTA), Dec 3-5 2012, pp. 1-8. Fremantle, WA: IEEE. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/9742 | |
dc.identifier.doi | 10.1109/DICTA.2012.6411675 | |
dc.description.abstract |
The multi-Bernoulli random finite set (MB-RFS) filter is a recent model for efficiently performing multi-target tracking in video by representing the state as a multi-modal distribution, incorporating data association and target detection into the model itself rather than having them as inputs from external subsystems that can be prone to failure. However, the MB-RFS is based on the non-Bayesian concept of random finite sets and its original derivation does not make it explicit what independence assumptions are being used. We show that the MB-RFS can in fact be reformulated as a purely Bayesian model, define the model and its independence assumptions explicitly and derive simpler update equations that are shown to be identical to the original RFS-based formulas. This equivalence may have implications for further theoretical research aimed at uncovering connections between random finite sets and `classical' Bayesian probability. In addition, a flaw in the original derivation of the MB-RFS is corrected and is shown to greatly improve the performance of the MB-RFS on two publicly available datasets: the VS-PETS 2003 soccer video and an ice hockey video. | |
dc.publisher | IEEE | |
dc.title | A bayesian formulation for multi-bernoulli random finite sets in multi-target tracking | |
dc.type | Conference Paper | |
dcterms.source.startPage | 1 | |
dcterms.source.endPage | 8 | |
dcterms.source.title | Digital Image Computing Techniques and Applications 2012 | |
dcterms.source.series | Digital Image Computing Techniques and Applications 2012 | |
dcterms.source.conference | DICTA 2012 | |
dcterms.source.conference-start-date | Dec 3 2012 | |
dcterms.source.conferencelocation | Perth, WA | |
dcterms.source.place | Not known | |
curtin.department | ||
curtin.accessStatus | Fulltext not available |