Multi-Object Particle Filter Revisited

Kim, D, Vo, B and Vo, B 2016, 'Multi-Object Particle Filter Revisited', in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2016), Ansan, South Korea, 27-29 October 2016, pp. 42-47.


Document type: Conference Paper
Collection: Conference Papers

Title Multi-Object Particle Filter Revisited
Author(s) Kim, D
Vo, B
Vo, B
Year 2016
Conference name ICCAIS 2016
Conference location Ansan, South Korea
Conference dates 27-29 October 2016
Proceedings title Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2016)
Publisher IEEE
Place of publication United States
Start page 42
End page 47
Total pages 6
Abstract Instead of the filtering density, we are interested in the entire posterior density that describes the random set of object trajectories. So far only Markov Chain Monte Carlo (MCMC) technique have been proposed to approximate the posterior distribution of the set of trajectories. Using labeled random finite set we show how the classical multi-object particle filter (a direct generalisation of the standard particle filter to the multi-object case) can be used to recursively compute posterior distribution of the set of trajectories. The result is a generic Bayesian multi-object tracker that does not require re-computing the posterior at every time step nor running a long Markov chain, and is much more efficient than the MCMC approximations.
Subjects Signal Processing
DOI - identifier 10.1109/ICCAIS.2016.7822433
Copyright notice © 2016 Crown
ISBN 9781509006502
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