A bayesian formulation for multi-bernoulli random finite sets in multi-target tracking
|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.|
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.title||A bayesian formulation for multi-bernoulli random finite sets in multi-target tracking|
|dcterms.source.title||Digital Image Computing Techniques and Applications 2012|
|dcterms.source.series||Digital Image Computing Techniques and Applications 2012|
|dcterms.source.conference-start-date||Dec 3 2012|
|curtin.accessStatus||Fulltext not available|