Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SIFT Features Tracking for Video Stabilization
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Evaluation of the SIFT Object Recognition Method in Mobile Robots
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Improved Simultaneous Computation of Motion Detection and Optical Flow for Object Tracking
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Video stabilization using kalman filter and phase correlation matching
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Digital image translational and rotational motion stabilization using optical flow technique
IEEE Transactions on Consumer Electronics
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
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A fast and efficient video stabilization method based on speeded-up robust features (SURF) is presented in this paper. The SURF features are extracted and tracked in each frame and then refined through Random Sample Consensus (RANSAC) to estimate the affine motion parameters. The intentional camera motions are filtered out through Adaptive Motion Vector Integration (AMVI). Experiments performed on several video streams illustrate superior performance of the SURF based video stabilization in terms of accuracy and speed when compared with the Scale Invariant Feature Transform (SIFT) based stabilization method.