Video stabilization using Speeded Up Robust Features

Video stabilization is one of the most important enhancement techniques used to remove undesired motion in a video. Combination of global camera motion estimation along with motion separation determines the undesired motion, which is to be compensated to produce a stable video sequence. In this paper a novel method for robust video stabilization is proposed which uses Speeded Up Robust Features(SURF) as stable feature points to be tracked between frames for global motion estimation. Different measures are taken to select the most appropriate feature point trajectories. A discrete kalman filter is used to smoothen so estimated motion vectors and the resultant stabilized video is obtained by compensating the unstable motion.

[1]  Sung-Jea Ko,et al.  Fast digital image stabilizer based on Gray-coded bit-plane matching , 1999, IEEE Trans. Consumer Electron..

[2]  Dan Schonfeld,et al.  Robust Video Stabilization Based on Particle Filter Tracking of Projected Camera Motion , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Andrea Fusiello,et al.  Image stabilization by features tracking , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[4]  Mario Fernando Montenegro Campos,et al.  An Improved Methodology for Image Feature Matching , 2009, 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing.

[5]  Wenbin Chen,et al.  Video Stabilization Using Scale-Invariant Features , 2007, 2007 11th International Conference Information Visualization (IV '07).

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

[7]  SchonfeldDan,et al.  Robust video stabilization based on particle filter tracking of projected camera motion , 2009 .

[8]  Joonki Paik,et al.  An adaptive motion decision system for digital image stabilizer based on edge pattern matching , 1992 .

[9]  Kamesh Namuduri,et al.  Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework , 2009, IEEE Transactions on Consumer Electronics.

[10]  Xinggang Lin,et al.  Digital image stabilization based on circular block matching , 2006, IEEE Transactions on Consumer Electronics.

[11]  Jyh-Yeong Chang,et al.  Digital image translational and rotational motion stabilization using optical flow technique , 2002, IEEE Trans. Consumer Electron..

[12]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[13]  Sebastiano Battiato,et al.  SIFT Features Tracking for Video Stabilization , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[14]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[15]  S. Erturk,et al.  Digital image stabilization with sub-image phase correlation based global motion estimation , 2003, IEEE Trans. Consumer Electron..