Accurate real-time tracking using mutual information

In this paper we present a direct tracking approach that uses Mutual Information (MI) as a metric for alignment. The proposed approach is robust, real-time and gives an accurate estimation of the displacement that makes it adapted to augmented reality applications. MI is a measure of the quantity of information shared by signals that has been widely used in medical applications. Since then, and although MI has the ability to perform robust alignment with illumination changes, multi-modality and partial occlusions, few works propose MI-based applications related to object tracking in image sequences due to some optimization problems. In this work, we propose an optimization method that is adapted to the MI cost function and gives a practical solution for augmented reality application. We show that by refining the computation of the Hessian matrix and using a specific optimization approach, the tracking results are far more robust and accurate than the existing solutions. A new approach is also proposed to speed up the computation of the derivatives and keep the same optimization efficiency. To validate the advantages of the proposed approach, several experiments are performed. The ESM and the proposed MI tracking approaches are compared on a standard dataset. We also show the robustness of the proposed approach on registration applications with different sensor modalities: map versus satellite images and satellite images versus airborne infrared images within different AR applications.