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MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
November 2005 (vol. 27 no. 11)
pp. 1805-1918
ASCII Text
x 
 
Zia Khan, Tucker Balch, Frank Dellaert, "MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1805-1918, November, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.223,
author = {Zia Khan and Tucker Balch and Frank Dellaert},
title = {MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {11},
issn = {0162-8828},
year = {2005},
pages = {1805-1918},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.223},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IS - 11
SN - 0162-8828
SP1805
EP1918
EPD - 1805-1918
A1 - Zia Khan,
A1 - Tucker Balch,
A1 - Frank Dellaert,
PY - 2005
KW - Index Terms- Particle filters
KW - multitarget tracking
KW - Markov random fields
KW - Markov chain Monte Carlo.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
We describe a particle filter that effectively deals with interacting targets—targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.

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Index Terms:
Index Terms- Particle filters, multitarget tracking, Markov random fields, Markov chain Monte Carlo.
Citation:
Zia Khan, Tucker Balch, Frank Dellaert, "MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1805-1918, Nov. 2005, doi:10.1109/TPAMI.2005.223
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