Robust contour-based object tracking integrating color and edge likelihoods

We present in this paper a novel object tracking system based on 3D contour models. For this purpose, we integrate two complimentary likelihoods, defined on local color statistics and intensity edges, into a common nonlinear estimation problem. The proposed method improves robustness and adaptivity with respect to challenging background and light conditions, and can be extended to multiple calibrated cameras. In order to achieve real-time capabilities for complex models, we also integrate in this framework a GPU-accelerated contour sampler, which quickly selects feature points and deals with generic shapes including polyhedral, non-convex as well as smooth surfaces, represented by polygonal meshes.