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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...
Nowadays, vehicle tracking is a vital approach to assist and improve the road traffic control, surveillance and security systems by having the detail of the captured vehicle information. In past, many tracking techniques have been implemented and suffered from the well known 'occlusion' problems. Increasing the accuracy of the tracking algorithm has caused the computational cost due to the inflexibility...
Vehicle detection and tracking is essential in traffic surveillance and traffic flow optimization. However, occlusion or overlapped vehicle tracking is difficult and remain a challenging research topic in image processing. In this paper, a conventional Markov Chain Monte Carlo (MCMC) is enhanced via Cumulative Sum (CUSUM) path plot in order to track vehicles in overlapping situation. By calculating...
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