Motion from occlusions

In computer vision, occlusions are almost always seen as undesirable singularities that pose difficult challenges to image motion analysis problems, such as optic flow computation, motion segmentation, disparity estimation, or egomotion estimation. However, it is well known that occlusions are extremely powerful cues for depth or motion perception, and could be used to improve those methods.

In this paper, we propose to recover camera motion information based uniquely on occlusions, by observing two specially useful properties: occlusions are independent of the camera rotation, and reveal direct information about the camera translation.

We assume a monocular observer, undergoing general rotational and translational motion in a static environment. We present a formal model for occlusion points and develop a method suitable for occlusion detection. Through the classification and analysis of the detected occlusion points, we show how to retrieve information about the camera translation (FOE). Experiments with real images are presented and discussed in the paper.