Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator

Wei Yeang Kow; Wei Leong Khong; Yit Kwong Chin; Ismail Saad; Kenneth Tze Kin Teo

doi:10.1109/CIMSim.2012.61

Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator

Wei Yeang Kow
, Wei Leong Khong
, Yit Kwong Chin
, Ismail Saad
, Kenneth Tze Kin Teo

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

2 Citations (Scopus)

Abstract

Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle's position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.

Original language	English
Title of host publication	4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012
Pages	270-275
Number of pages	6
DOIs	https://doi.org/10.1109/CIMSim.2012.61
Publication status	Published - 2012
Externally published	Yes
Event	International Conference on Computational Intelligence, Modelling and Simulation 2012 - Kuantan, Malaysia Duration: 25 Sept 2012 → 27 Sept 2012 Conference number: 4th https://ieeexplore.ieee.org/xpl/conhome/6336543/proceeding (Proceedings)

Publication series

Name	Proceedings of International Conference on Computational Intelligence, Modelling and Simulation
ISSN (Print)	2166-8523

Conference

Conference	International Conference on Computational Intelligence, Modelling and Simulation 2012
Abbreviated title	CSSim 2012
Country/Territory	Malaysia
City	Kuantan
Period	25/09/12 → 27/09/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6336543/proceeding (Proceedings)

Keywords

CUSUM path plot
Genetic operator (GO)
Markov Chain Monte Carlo (MCMC)
Variance ratio (VR)

Access to Document

10.1109/CIMSim.2012.61

Cite this

Kow, W. Y., Khong, W. L., Chin, Y. K., Saad, I., & Teo, K. T. K. (2012). Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator. In 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012 (pp. 270-275). (Proceedings of International Conference on Computational Intelligence, Modelling and Simulation). https://doi.org/10.1109/CIMSim.2012.61

Kow, Wei Yeang ; Khong, Wei Leong ; Chin, Yit Kwong et al. / Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator. 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012. 2012. pp. 270-275 (Proceedings of International Conference on Computational Intelligence, Modelling and Simulation).

@inproceedings{c4626710bdf340439e0cb90aa2955f24,

title = "Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator",

abstract = "Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle's position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.",

keywords = "CUSUM path plot, Genetic operator (GO), Markov Chain Monte Carlo (MCMC), Variance ratio (VR)",

author = "Kow, \{Wei Yeang\} and Khong, \{Wei Leong\} and Chin, \{Yit Kwong\} and Ismail Saad and Teo, \{Kenneth Tze Kin\}",

note = "Copyright: Copyright 2013 Elsevier B.V., All rights reserved.; International Conference on Computational Intelligence, Modelling and Simulation 2012, CSSim 2012 ; Conference date: 25-09-2012 Through 27-09-2012",

year = "2012",

doi = "10.1109/CIMSim.2012.61",

language = "English",

series = "Proceedings of International Conference on Computational Intelligence, Modelling and Simulation",

pages = "270--275",

booktitle = "4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012",

url = "https://ieeexplore.ieee.org/xpl/conhome/6336543/proceeding",

}

Kow, WY, Khong, WL, Chin, YK, Saad, I & Teo, KTK 2012, Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator. in 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012. Proceedings of International Conference on Computational Intelligence, Modelling and Simulation, pp. 270-275, International Conference on Computational Intelligence, Modelling and Simulation 2012, Kuantan, Malaysia, 25/09/12. https://doi.org/10.1109/CIMSim.2012.61

Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator. / Kow, Wei Yeang; Khong, Wei Leong; Chin, Yit Kwong et al.
4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012. 2012. p. 270-275 (Proceedings of International Conference on Computational Intelligence, Modelling and Simulation).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator

AU - Kow, Wei Yeang

AU - Khong, Wei Leong

AU - Chin, Yit Kwong

AU - Saad, Ismail

AU - Teo, Kenneth Tze Kin

N1 - Conference code: 4th

PY - 2012

Y1 - 2012

N2 - Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle's position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.

AB - Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle's position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.

KW - CUSUM path plot

KW - Genetic operator (GO)

KW - Markov Chain Monte Carlo (MCMC)

KW - Variance ratio (VR)

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DO - 10.1109/CIMSim.2012.61

M3 - Conference Paper

AN - SCOPUS:84872550663

T3 - Proceedings of International Conference on Computational Intelligence, Modelling and Simulation

SP - 270

EP - 275

BT - 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012

T2 - International Conference on Computational Intelligence, Modelling and Simulation 2012

Y2 - 25 September 2012 through 27 September 2012

ER -

Kow WY, Khong WL, Chin YK, Saad I, Teo KTK. Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator. In 4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012. 2012. p. 270-275. (Proceedings of International Conference on Computational Intelligence, Modelling and Simulation). doi: 10.1109/CIMSim.2012.61

Enhancement of markov chain monte carlo convergence speed in vehicle tracking using genetic operator

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Keywords

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