Adaptive line tracking with multiple hypotheses for augmented reality

We present a real-time model-based line tracking approach with adaptive learning of image edge features that can handle partial occlusion and illumination changes. A CAD (VRML) model of the object to track is needed. First, the visible edges of the model with respect to the camera pose estimate are sorted out by a visibility test performed on standard graphics hardware. For every sample point of every projected visible 3D model line a search for gradient maxima in the image is then carried out in a direction perpendicular to that line. Multiple hypotheses of these maxima are considered as putative matches. The camera pose is updated by minimizing the distances between the projection of all sample points of the visible 3D model lines and the most likely matches found in the image. The state of every edge's visual properties is updated after each successful camera pose estimation. We evaluated the algorithm and showed the improvements compared to other tracking approaches.