Robust Recovery of Ego-Motion

A robust method is introduced for computing the camera motion (the ego-motion) in a static scene. The method is based on detecting a single planar surface in the scene directly from image intensities, and computing its 2D motion in the image plane. The detected 2D motion of the planar surface is used to register the images, so that the planar surface appears stationary. The resulting displacement field for the entire scene in such registered frames is affected only by the 3D translation of the camera, which is computed by finding the focus-of-expansion in the registered frames. This step is followed by computing the 3D rotation to complete the computation of the ego-motion.

[1]  Michal Irani,et al.  Recovery of ego-motion using image stabilization , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Kenji Mase,et al.  Simultaneous multiple optical flow estimation , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[3]  Michal Irani,et al.  Detecting and Tracking Multiple Moving Objects Using Temporal Integration , 1992, ECCV.

[4]  Azriel Rosenfeld,et al.  Multiresolution image processing and analysis , 1984 .

[5]  S. Negahdaripour,et al.  Motion recovery from image sequences using First-order optical flow information , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[6]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  K. Hanna.  Direct multi-resolution estimation of ego-motion and structure from motion , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[8]  P. Burt,et al.  Mechanisms for isolating component patterns in the sequential analysis of multiple motion , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[9]  Michal Irani,et al.  Image sequence enhancement using multiple motions analysis , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  M. Ali Taalebinezhaad.  Direct recovery of motion and shape in the general case by fixation , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[11]  Gilad Adiv,et al.  Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Yiannis Aloimonos,et al.  Active Egomotion Estimation: A Qualitative Approach , 1992, ECCV.

[13]  Patrick Bouthemy,et al.  Estimation of time-to-collision maps from first order motion models and normal flows , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[14]  M. Tistarelli,et al.  Direct estimation of time-to-impact from optical flow , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[15]  S. Ullman,et al.  Direct computation of the focus of expansion from velocity field measurements , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[16]  D. T. Lawton,et al.  The Use of Difference Fields in Processing Sensor Motion , 1983 .

[17]  Shmuel Peleg,et al.  Computing two motions from three frames , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[18]  A. Jepson,et al.  A fast subspace algorithm for recovering rigid motion , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[19]  M. Ali Taalebinezhaad.  Direct Recovery of Motion and Shape in the General Case by Fixation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Liuqing Huang,et al.  Relative depth from motion using normal flow: an active and purposive solution , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[21]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[22]  H C Longuet-Higgins,et al.  The visual ambiguity of a moving plane , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.