Adaptive data association for multi-target tracking using relaxation

Abstract

This paper introduces an adaptive algorithm determining the measurement-track association problem in multi-target tracking. We model the target and measurement relationships and then define a MAP estimate for the optimal association. Based on this model, we introduce an energy function defined over the measurement space, that incorporates the natural constraints for target tracking. To find the minimizer of the energy function, we derived a new adaptive algorithm by introducing the Lagrange multipliers and local dual theory. Through the experiments, we show that this algorithm is stable and works well in general environments. © Springer-Verlag Berlin Heidelberg 2005.