A multi-target tracking algorithm based on Gaussian mixture model

Abstract

Since the joint probabilistic data association (JPDA) algorithm results in calculation explosion with the increasing number of targets, a multi-target tracking algorithm based on Gaussian mixture model (GMM) clustering is proposed. The algorithm is used to cluster the measurements, and the association matrix between measurements and tracks is constructed by the posterior probability. Compared with the traditional data association algorithm , this algorithm has better tracking performance and less computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.