MCMC particle filter-based vehicle tracking method using multiple hypotheses and appearance model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this study, we propose a multiple vehicle tracking method using multiple hypotheses and the appearance model. The multiple hypotheses are associated with multiple tracks using track-to-multiple hypotheses association method. A target state is estimated using the maximum a posteriori probability estimation method. The posterior probability is proportional to the product of a priori probability and the likelihood that is calculated using similarities of multiple hypotheses and the appearance model. The posterior probability density function is estimated using the Markov chain Monte Carlo particle filter. An optimal posterior target state is determined using a sample with the maximum a posteriori probability. Our experimental results show that the proposed method can improve multiple objects tracking precision as well as multiple object tracking accuracy.

Original languageEnglish
Title of host publication2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
Pages1131-1136
Number of pages6
DOIs
StatePublished - 2013
Event2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013 - Gold Coast, QLD, Australia
Duration: 23 Jun 201326 Jun 2013

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
Country/TerritoryAustralia
CityGold Coast, QLD
Period23/06/1326/06/13

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