Adaptive Tracking of Overlapping Vehicles Via Markov Chain Monte Carlo with CUSUM Path Plot Algorithm

Abstract

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 the hairiness of CUSUM path plot, MCMC can be diagnosed as converged based on its sampling outputs. Varying sample size of MCMC provides enhancement to the tracking performance and capability of overcoming the limitation of conventional fix sample size algorithm. In addition, implementation of m-th order prior probability distribution and fusion of color and edge distance likelihood have further improved the tracking accuracy. MCMC with fixed sample size and CUSUM path plot are implemented and their corresponding performances are analyzed. Experimental results show that MCMC with CUSUM path plot has better performance where it is able to track the overlapped vehicle accurately with lesser processing time.