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

Title
MCMC particle filter-based vehicle tracking method using multiple hypotheses and appearance model
Authors
Lim, Young-ChulKim, DongyoungLee, Chung-Hee
DGIST Authors
Lim, Young-Chul; Kim, Dongyoung; Lee, Chung-Hee
Issue Date
2013
Citation
2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013, 1131-1136
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
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. © 2013 IEEE.
URI
http://hdl.handle.net/20.500.11750/3813
DOI
10.1109/IVS.2013.6629618
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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