Model-based object tracking in cluttered scenes with occlusions

In this paper, we propose an efficient method for tracking 3D modelled objects in clustered scenes. Rather than tracking objects in the image, our approach relies on the object recognition aspect of tracking, Candidate matches between image and model features define volumes in the space of transformations, The volumes of the pose space satisfying the maximum number of correspondences are those that best align the model with the image. Object motion defines a trajectory in the pose space. We give some results showing that the presented method allows to track objects even when they are totally occluded for a short while, without supposing any motion model and with a low computational cost (below 200 ms per frame on a basic workstation). Furthermore, this algorithm can also be used to initialize the tracking.