Multibody motion segmentation using the geometry of 6 points in 2D images.

We propose a method for segmenting an arbitrary number of moving objects using the geometry of 6 points in 2D images to infer motion consistency. This geometry allows us to determine whether or not observations of 6 points over several frames are consistent with a rigid 3D motion. The matching between observations of the 6 points and an estimated model of their configuration in 3D space, is quantified in terms of a geometric error derived from distances between the points and 6 corresponding lines in the image. This leads to a simple motion inconsistency score, based on the geometric errors of 6points that in the ideal case should be zero when the motion of the points can be explained by a rigid 3D motion. Initial point clusters are determined in the spatial domain and merged in motion trajectory domain based on this score. Each point is then assigned to the cluster, which gives the lowest score.Our algorithm has been tested with real image sequences from the Hopkins155 database with very good results, competing withthe state of the art methods, particularly for degenerate motion sequences. In contrast to the motion segmentation methods basedon multi-body factorization, that assume an affine camera model, the proposed method allows the mapping from 3D space to the 2D image to be fully projective