Energy minimization approach for online data association with missing data

Data association problem is of crucial importance to improve online target tracking performance in many difficult visual environments. Usually, association effectiveness is based on prior information and observation category. However, problems can happened when targets are quite similar. Therefore, neither the color, nor the shape could be helpful informations to achieve the task of data association. Likewise, problems can also arise for target tracking, under the constraint of missing data, with complex motions and randomly deformations over time. Such restrictions, i.e. the lack in prior information, limit the association performance. To remedy, we propose a novel method for data association, inspired from the evolution of the target dynamic models, and based on a global minimization of an energy vector. The main idea is to measure the absolute geometric accuracy between features. The parameterless constitutes the main advantage of our energy minimization approach: only one information, the position, is used as input to our algorithm. We have tested our approach on several sequences to show its effectiveness.