Enhancement of Particle Filter Approach for Vehicle Tracking Via Adaptive Resampling Algorithm

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 to adapt the partial and fully occluded situations. Besides occlusion, appearance of new objects and background noises in the captured videos increase the difficulties of continuously tracking the labelled vehicles. In this paper, an adaptive particle filter approach has been proposed as the tracking algorithm to solve the vehicle occlusion problem. In order to solve the common particle filter degeneracy problem, the proposed particle filter is equipped with the adaptive resampling algorithm which is capable of dealing with various occlusion incidents. The experimental results show that enhancement of the particle filter via resampling algorithm has been robustly tracking the vehicles, and significantly improve the accuracy in tracking the occluded vehicles without compromising the processing time.