Spatio-temporal extraction of articulated models in a graph pyramid
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Sparse motion segmentation using multiple six-point consistencies
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Hierarchical spatio-temporal extraction of models for moving rigid parts
Pattern Recognition Letters
| Hi-index | 0.00 |
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 that is derived from the geometric errors of 6 points, that in the ideal case should be zero when the motion of the points can be explained by a rigid 3D motion. Initial clusters are determined in the spatial domain and merged in motion trajectory domain based on the score. Each point is then assigned to a cluster by assigning the point to the segment of the lowest score. Our algorithm has been tested with real image sequences from the Hopkins155 database with very good results, competing with the state of the art methods, particularly for degenerate motion sequences. In contrast the motion segmentation methods based on multi-body factorization, that assumes an affine camera model, the proposed method allows the mapping from the 3D space to the 2D image to be fully projective.