Video Stabilization Using Scale-Invariant Features

Video Stabilization is one of those important video processing techniques to remove the unwanted camera vibration in a video sequence. In this paper, we present a practical method to remove the annoying shaky motion and reconstruct a stabilized video sequence with good visual quality. Here, the scale invariant (SIFT) features, proved to be invariant to image scale and rotation, is applied to estimate the camera motion. The unwanted vibrations are separated from the intentional camera motion with the combination of Gaussian kernel filtering and parabolic fitting. It is demonstrated that our method effectively removes the high frequency 'noise' motion, but also minimize the missing area as much as possible. To reconstruct the undefined areas, resulting from motion compensation, we adopt the mosaicing method with Dynamic Programming. The proposed method has been confirmed to be effective over a widely variety of videos.

[1]  Harry Shum,et al.  Full-frame video stabilization with motion inpainting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Janusz Konrad,et al.  Probabilistic video stabilization using Kalman filtering and mosaicing , 2003, IS&T/SPIE Electronic Imaging.

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

[4]  K. Ratakonda.  Real-time digital video stabilization for multi-media applications , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[5]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Tai-Pang Wu,et al.  Video repairing: inference of foreground and background under severe occlusion , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Eli Shechtman,et al.  Space-time video completion , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[8]  Guangyou Xu,et al.  Digital Video Sequence Stabilization Based on 2.5D Motion Estimation and Inertial Motion Filtering , 2001, Real Time Imaging.

[9]  George Loizou,et al.  Computer vision and pattern recognition , 2007, Int. J. Comput. Math..

[10]  Harry Shum,et al.  Full-frame video stabilization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  James Davis,et al.  Mosaics of scenes with moving objects , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  Matthew A. Brown,et al.  Recognising panoramas , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Adam Baumberg,et al.  Reliable feature matching across widely separated views , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[14]  Michael Bosse,et al.  Non-metric image-based rendering for video stabilization , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Shang-Hong Lai,et al.  A robust and efficient video stabilization algorithm , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).