CUSUM-variance ratio based Markov chain Monte Carlo algorithm in overlapped vehicle tracking

Kow, Wei Yeang and Khong, Wei Leong and Chin, Yit Kwong and Ismail Saad and Teo, Kenneth Tze Kin (2011) CUSUM-variance ratio based Markov chain Monte Carlo algorithm in overlapped vehicle tracking. In: 2011 IEEE Conference on Computer Applications and Industrial Electronics, ICCAIE 2011, 4-7 December 2011, Penang, Malaysia.

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Markov Chain Monte Carlo (MCMC) is one of the algorithms that have been widely implemented in tracking vehicle for traffic surveillance purposes. The sampling efficiency of the algorithm is essential to determine the vehicle position accurately. However, the sample size of the algorithm is still remaining an issue as non-optimal sample size will defect the tracking accuracy, especially when the moving vehicle is overlapped. Adaptive sample size of MCMC has been implemented using CUSUM Path Plot and Variance Ratio algorithms to perform vehicle tracking. CUSUM Path Plot determines the samples convergence rate by calculating the hairiness of the sample size whereas Variance Ratio method computes two sets of MCMC to determine the samples steady state. This paper proposes the fusion of CUSUM-Variance ratio algorithm to enhance the tracking efficiency. Experimental results shows that the CUSUM-Variance Ratio method have a better performance in tracking the overlapping vehicle with higher accuracy and more optimal sample size compared to the standalone CUSUM Path Plot and Variance Ratio approaches.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: CUSUM Path Plot, Markov Chain Monte Carlo, MCMC, Variance Ratio, Vehicle Tracking
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics
Depositing User: ADMIN ADMIN
Date Deposited: 12 Jul 2012 07:46
Last Modified: 08 Sep 2014 08:11

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