Fusion tracking in color and infrared images using sequential belief propagation

In this paper, we propose an approach to fuse the color and infrared images for visual tracking. The contribution of this paper is twofold: First, we use the covariance feature to construct the likelihood function under the framework of particle filter. This likelihood captures the spatial and statistical properties as well as their correlation within representation of covariance. Secondly, different from the existing fusion approaches, our approach automatically realizes the fusion by sequential belief propagation, which uses message passing scheme to exchange information between color and infrared image. The performance of the proposed approach is evaluated using real visual tracking examples.