Shooting a smooth video with a shaky camera

Abstract. The image sequence in a video taken by a moving camera may suffer from irregular perturbations because of irregularities in the motion of the person or vehicle carrying the camera. We show how to use information in the image sequence to correct the effects of these irregularities so that the sequence is smoothed, i.e., is approximately the same as the sequence that would have been obtained if the motion of the camera had been smooth. Our method is based on the fact that the irregular motion is almost entirely rotational, and that the rotational image motion can be detected and corrected if a distant object, such as the horizon, is visible.

[1]  G. Stewart.  Introduction to matrix computations , 1973 .

[2]  Azriel Rosenfeld,et al.  Image Sequence Stabilization in Real Time , 1996, Real Time Imaging.

[3]  Azriel Rosenfeld,et al.  Egomotion analysis based on the Frenet-Serret motion model , 2005, International Journal of Computer Vision.

[4]  M. G. Bekker.  Introduction to Terrain-Vehicle Systems , 1969 .

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

[6]  Nicola Ancona,et al.  Optical flow from 1-D correlation: Application to a simple time-to-crash detector , 1993, 1993 (4th) International Conference on Computer Vision.

[7]  J R Ellis,et al.  Vehicle Handling Dynamics , 1994 .

[8]  Rama Chellappa,et al.  Fast structure from motion recovery applied to 3D image stabilization , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[9]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[10]  Kazuo Yamafuji,et al.  Postural stability of a human riding a unicycle and its emulation by a robot , 1997, IEEE Trans. Robotics Autom..

[11]  James H. Williams.  Fundamentals of Applied Dynamics , 1995 .

[12]  Takeo Kanade,et al.  Development of a video-rate stereo machine , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[13]  Alessandro Verri,et al.  Against Quantitative Optical Flow , 1987 .

[14]  A. Remole.  PERCEPTION WITH AN EYE FOR MOTION , 1987 .

[15]  Yiannis Aloimonos,et al.  Estimating the heading direction using normal flow , 1994, International Journal of Computer Vision.

[16]  Jan J. Koenderink,et al.  Solid shape , 1990 .

[17]  Rufus Oldenburger,et al.  Mathematical engineering analysis , 1953 .

[18]  Azriel Rosenfeld,et al.  The Applicability of Green's Theorem to Computation of Rate of Approach , 1999, International Journal of Computer Vision.

[19]  V. M. Zat︠s︡iorskiĭ.  Kinematics of human motion , 1998 .

[20]  Shmuel Peleg,et al.  Rectified mosaicing: mosaics without the curl , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[21]  Kristin J. Dana,et al.  Real-time scene stabilization and mosaic construction , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[22]  Rama Chellappa,et al.  Model-based vehicular motion and structure estimation , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[23]  Andrea Giachetti,et al.  The use of optical flow for road navigation , 1998, IEEE Trans. Robotics Autom..

[24]  Rama Chellappa,et al.  Selective stabilization of images acquired by unmanned ground vehicles , 1996, Proceedings of 13th International Conference on Pattern Recognition.