Robust statistics for 3D object tracking

This paper focuses on methods that enhance performance of a model based 3D object tracking system. Three statistical methods and an improved edge detector are discussed and compared. The evaluation is performed on a number of characteristic sequences incorporating shift, rotation, texture, weak illumination and occlusion. Considering the deviations of the pose parameters from ground truth, it is shown that improving the measurements´ accuracy in the detection step yields better results than improving contaminated measurements with statistical means