Multiple Objects Tracking Method Based on Particle Filter

Object tracking is a challenging problem due to the presence of noise, occlusion, clutter and dynamic change in the scene other than the motion of the object of interest. A variety of tracking algorithms has been proposed and implemented to overcome the related difficulties, but there are still some problems need to be covered. In this paper, we present an approach for multiple objects tracking based on particle filter algorithm. We use the particle filter to predict the trajectory of the target. The problem of occlusion is predicted based on the likelihood measurement and estimated samples distance. The particle filter approximates a posterior probability density of the state using samples or particles. Each state is denoted as the hypothetical state of the tracked object and its weight which is predicted based on the system model. In this paper, the state is treated as a position, speed, size, scale and appearance of the object. The samples weight is considered as the likelihood of each particle which is measured based on the similarity between the colour feature of the target model and the objects. And finally, the mean state of the particles is treated as the estimated state of the object. The experiments are performed to confirm the effectiveness of the method to track multiple objects.