Affiliation:
1. School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, P.R. China
Abstract
Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.
Funder
Natural Science Foundation of Liaoning Province
Program for Liaoning Innovative Talents in University
National Natural Science Foundation of China
Subject
Applied Mathematics,Control and Optimization,Instrumentation
Cited by
5 articles.
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