Comparison of Credal Assignment Algorithms in Kinematic Data Tracking Context

Abstract

This paper compares several assignment algorithms in a multi-target tracking context, namely: the optimal Global Nearest Neighbor algorithm (GNN) and a few based on belief functions. The robustness of the algorithms are tested in different situations, such as: nearby targets tracking, targets appearances management. It is shown that the algorithms performances are sensitive to some design parameters. It is shown that, for kinematic data based assignment problem, the credal assignment algorithms do not outperform the standard GNN algorithm. © Springer International Publishing Switzerland 2014.