Image sequence stabilization in real time
Real-Time Imaging
Fast electronic digital image stabilization for off-road navigation
Real-Time Imaging
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A Smoothing Filter for CONDENSATION
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Fast 3D Stabilization and Mosaic Construction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Stabilization by Features Tracking
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Color-Based Video Stabilization for Real-Time On-Board Object Detection on High-Speed Trains
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Embedding Motion in Model-Based Stochastic Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Full-Frame Video Stabilization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast particle smoothing: if I had a million particles
ICML '06 Proceedings of the 23rd international conference on Machine learning
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Monte Carlo smoothing with application to audio signal enhancement
IEEE Transactions on Signal Processing
Fast digital image stabilizer based on Gray-coded bit-plane matching
IEEE Transactions on Consumer Electronics
Digital image translational and rotational motion stabilization using optical flow technique
IEEE Transactions on Consumer Electronics
Digital image stabilization with sub-image phase correlation based global motion estimation
IEEE Transactions on Consumer Electronics
A robust global motion estimation for digital video stabilization
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Video stabilisation using local salient feature in particle filter framework
International Journal of Wireless and Mobile Computing
Computer Vision and Image Understanding
Video stabilization using maximally stable extremal region features
Multimedia Tools and Applications
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Video stabilization is an important technique in digital cameras. Its impact increases rapidly with the rising popularity of handheld cameras and cameras mounted on moving platforms (e.g., cars). Stabilization of two images can be viewed as an image registration problem. However, to ensure the visual quality of the whole video, video stabilization has a particular emphasis on the accuracy and robustness over long image sequences. In this paper, we propose a novel technique for video stabilization based on the particle filtering framework. We extend the traditional use of particle filters in object tracking to tracking of the projected affine model of the camera motions. We rely on the inverse of the resulting image transform to obtain a stable video sequence. The correspondence between scale-invariant feature transform points is used to obtain a crude estimate of the projected camera motion. We subsequently postprocess the crude estimate with particle filters to obtain a smooth estimate. It is shown both theoretically and experimentally that particle filtering can reduce the error variance compared to estimation without particle filtering. The superior performance of our algorithm over other methods for video stabilization is demonstrated through computer simulated experiments.