Browsing by Author "Wei Leong Khong"

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    Enhancement of Markov Chain Monte Carlo Convergence Speed in Vehicle Tracking Using Genetic Operator
    (Modeling, Simulation & Computing Laboratory, Minerals & Materials Research Unit School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu, Malaysia, 2012) Wei Yeang Kow; Wei Leong Khong; Yit Kwong Chin; Ismail Saad; Kenneth Tze Kin Teo
    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.
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    Enhancement of Particle Filter Resampling in Vehicle Tracking via Genetic Algorithm
    (UKSim-AMSS 6th European Modelling Symposium, 2012) Wei Leong Khong; Wei Yeang Kow; Yit Kwong Chin; Mei Yeen Choong; Kenneth Tze Kin Teo
    Vehicle tracking is an essential approach that can help to improve the traffic surveillance or assist the road traffic control. Recently, the development of video surveillance infrastructure has incited the researchers to focus on the vehicle tracking by using video sensors. However, the amount of the on-road vehicle has been increased dramatically and hence the congestion of the traffic has made the occlusion scene become a challenge task for video sensor based tracking. Conventional particle filter will encounter tracking error during and after occlusion. Besides that, it also required more iteration to continuously track the vehicle after occlusion. Thus, particle filter with genetic operator resampling has been proposed as the tracking algorithm to faster converge and keep track on the target vehicle under various occlusion incidents. The experimental results show that enhancement of the particle filter with genetic algorithm manage to reduce the particle sample size.
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    Q-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network
    (2012) Yit Kwong Chin; Wei Yeang Kow; Wei Leong Khong; Min Keng Tan; Kenneth Tze Kin Teo
    Traffic flow optimization within traffic networks has been approached through different kinds of methods. One of the methods is to reconfigure the traffic signal timing plan. However, dynamic characteristic of the traffic flow is not able to be resolved by the conventional traffic signal timing plan management. As a result, traffic congestion still remains as an unsolved problem. Thus, in this study, artificial intelligence algorithm has been introduced in the traffic signal timing plan to enable the traffic management systems’ learning ability. QLearning algorithm acts as the learning mechanism for traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each other to a common goal of ensuring the fluency of the traffic flows within traffic network. The experimental results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimized the traffic flow.