Energetic Particle Filter for Online Multiple Target Tracking

Online target tracking requires to solve two problems: data association and online dynamic estimation. Usually, association effectiveness is based on prior information and observation category. However, problems can occur for tracking quite similar targets under the constraints of missing data and complex motions. The lack in prior information limits the association performance. To remedy, we propose a novel method for data association inspired from the evolution of target´s dynamic model and given by a global minimization of an energy. The concept amounts to measure the absolute geometric accuracy between features. The main advantage of our approach is that it is parameterless. We also integrate our method into the classical particle filter, that leads to what we call the energetic particle filter (EPF).