Mutual Information-Based Piecewise Planar Object Tracking

This master thesis deals with a template based tracking algorithm for piecewise planar objects. It is robust against changes in the appearance of the object (occlusion, illumination variation, specularities). The version that we propose supports multiple cameras. The method consists in minimizing the error between the observed images of the object and the warped images of the planes. We use for that an estimation of the pose of the object, which is to say a rigid 3D transformation. The robustness is obtained by using the mutual information as registration function. The main drawback of the mutual information is that it has a high computation complexity. We use an inverse composition approach for the warp update, so that pre-computations can be done and it decreases the complexity of the algorithm. We develop a way of computing the warp update and analyse the impact of this initiative on the optimization process. We also determine the optimal parameters for running the algorithms