Region clutter estimation method for multi-target tracking

Gaussian mixture particle probability hypothesis density (PHD) filter often assumes that the clutter density parameters are known. This method is impractical for real applications. In addition, the parameter values of the clutter points are usually dependent on environmental conditions, and they may change over time. Therefore, it is desirable for multiple-target tracking algorithm in real time to estimate the clutter density parameters. In this paper, a method of the clutter estimation about multi-target tracking is presented. Firstly, we estimate the number of clutter points in the scene online. Secondly, we estimate the clutter number and intensity in each region of interest. Simulation results show that its tracking performance is much better than those of multiple-target tracking algorithms which have not estimated the clutter intensity in complex situations and that it improves the real-time tracking and tracking accuracy.