Motion segmentation and local structure

The author presents a means of segmenting independently moving objects from rigid backgrounds and recovering the orientations of the normals of local planar surface patches. The camera motion can be arbitrary with the proviso that the independent image motion violates the epipolar constraint. The method relies on describing the motion of points in the world in terms of their angular velocity relative to the camera. The method is crucially dependent on estimating camera rotation around image directions. An assumption about the likely motion of automatic guided vehicles is used to resolve the two-fold ambiguity in the estimate of the normal of a local planar patch. This allows local scene structure and camera rotation to be estimated. Motion segmentation is demonstrated on a real image sequence.<<ETX>>

[1]  D. W. Murray,et al.  Coarse Image Motion for Saccade Control , 1992 .

[2]  Peter J. Burt,et al.  Object tracking with a moving camera , 1989, [1989] Proceedings. Workshop on Visual Motion.

[3]  David J. Heeger,et al.  Optical flow from spatialtemporal filters , 1987 .

[4]  S. Maybank,et al.  The angular Velocity associated with the optical flowfield arising from motion through a rigid environment , 1985, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[5]  H. C. Longuet-Higgins The reconstruction of a plane surface from two perspective projections , 1986, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[6]  Andrew Blake,et al.  Planar region detection and motion recovery , 1993, Image Vis. Comput..

[7]  Allen M. Waxman,et al.  Closed from solutions to image flow equations for planar surfaces in motion , 1986, Comput. Vis. Graph. Image Process..

[8]  Andrew Zisserman,et al.  Cooperating motion processes , 1991 .

[9]  O. Faugeras,et al.  The calibration problem for stereoscopic vision , 1989 .

[10]  Ramesh C. Jain,et al.  Segmentation of Frame Sequences Obtained by a Moving Observer , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Randal C. Nelson,et al.  Using Flow Field Divergence For Obstacle Avoidance: Towards Qualitative Vision , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[12]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch , 1981 .