Real time target tracking based on nonlinear mean shift and particle filters

Abstract

In radar tracking guidance, intelligent video surveillance, robot vision system, the parameters of position and velocity and steering state often need to get the target of interest, based on the motion characteristics of the target and further to control it. The filtering method is used to estimate the desired state parameters based on the functional relationship between the measured values and the state variables. This method is also called target tracking technique. At present, there are many target tracking technologies for different systems, but there is a big gap between the robustness and real-time requirements of the actual system. In order to solve the problem of large computation and bad real-time performance of Particle Filters, a real-time target tracking algorithm based on nonlinear mean shift and Particle Filters is proposed. The distribution of particles is closer to the actual posterior distribution by selecting the important probability density function. Furthermore, the nonlinear mean shift algorithm is integrated into the Particle Filters, so that the particles are further clustered into the real distribution. Finally, the algorithm is applied in the traffic video surveillance, and the effective tracking of the target motorcycle and vehicle is realized.