Camera Motion Quantification and Alignment

We propose a method to synchronize video sequences of distinct scenes captured by cameras undergoing similar motions. For the general camera motion and 3D scene, the camera ego-motions are featured by fundamental ratios obtained from the fundamental matrices. In the case of pure translation, translational magnitude features is used. These extracted features are invariant to the camera internal parameters, and therefore can be computed without recovering camera trajectories along the image sequences. Consequently, the alignment problem reduces to matching sets of feature vectors, obtained without any knowledge of other sequences. Experimental results demonstrate the accuracy and applications of the proposed method

[1]  J. W. Humberston Classical mechanics , 1980, Nature.

[2]  Denis Simakov,et al.  Feature-Based Sequence-to-Sequence Matching , 2006, International Journal of Computer Vision.

[3]  Yaron Caspi,et al.  Alignment of non-overlapping sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Prosenjit Bose,et al.  Temporal Synchronization of Video Sequences in Theory and in Practice , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[5]  Michael Werman,et al.  The viewing graph , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Kiriakos N. Kutulakos,et al.  Linear Sequence-to-Sequence Alignment , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Mubarak Shah,et al.  Accurate motion layer segmentation and matting , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Luc Van Gool,et al.  Synchronizing video sequences , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Wu Zhong International Trends of Pattern Recognition Research A Brief Introduction to the 18th International Conference on Pattern Recognition , 2006 .

[10]  Tanveer F. Syeda-Mahmood,et al.  View-invariant alignment and matching of video sequences , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  Lior Wolf,et al.  Sequence-to-Sequence Self Calibration , 2002, ECCV.

[13]  Philip H. S. Torr,et al.  Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting , 2002, International Journal of Computer Vision.

[14]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[15]  Patrick Pérez,et al.  Periodic motion detection and segmentation via approximate sequence alignment , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[16]  Gideon P. Stein,et al.  Tracking from multiple view points: Self-calibration of space and time , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[17]  Ian D. Reid,et al.  Synchronizing Image Sequences of Non-Rigid Objects , 2003, BMVC.

[18]  Xiaochun Cao,et al.  A new framework for video cut and paste , 2006, 2006 12th International Multi-Media Modelling Conference.