Proceedings ArticleDOI
Probabilistic video stabilization using Kalman filtering and mosaicing
A. Litvin,Janusz Konrad,William Clement Karl +2 more
- Vol. 5022, pp 663-674
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TLDR
This paper presents a new image processing method to remove unwanted vibrations and reconstruct a video sequence void of sudden camera movements based on a probabilistic estimation framework, and shows a significant improvement in stabilization quality.Abstract:
The removal of unwanted, parasitic vibrations in a video sequence induced by camera motion is an essential part of video acquisition in industrial, military and consumer applications. In this paper, we present a new image processing method to remove such vibrations and reconstruct a video sequence void of sudden camera movements. Our approach to separating unwanted vibrations from intentional camera motion is based on a probabilistic estimation framework. We treat estimated parameters of interframe camera motion as noisy observations of the intentional camera motion parameters. We construct a physics-based state-space model of these interframe motion parameters and use recursive Kalman filtering to perform stabilized camera position estimation. A six-parameter affine model is used to describe the interframe transformation, allowing quite accurate description of typical scene changes due to camera motion. The model parameters are estimated using a p-norm-based multi-resolution approach. This approach is robust to model mismatch and to object motion within the scene (which are treated as outliers). We use mosaicking in order to reconstruct undefined areas that result from motion compensation applied to each video frame. Registration between distant frames is performed efficiently by cascading interframe affine transformation parameters. We compare our method' s performance with that of a commercial product on real-life video sequences, and show a significant improvement in stabilization quality for our method.read more
Citations
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Full-frame video stabilization with motion inpainting
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Inertially stabilized platform technology Concepts and principles
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Video stabilization using robust feature trajectories
TL;DR: A method to directly stabilize a video without explicitly estimating camera motion, thus assuming neither motion models nor dominant motion is proposed and can deal with complicated videos containing near, large and multiple moving objects.
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Robust Video Stabilization Based on Particle Filter Tracking of Projected Camera Motion
TL;DR: A novel technique for video stabilization based on the particle filtering framework that extends the traditional use of particle filters in object tracking to tracking of the projected affine model of the camera motions and relies on the inverse of the resulting image transform to obtain a stable video sequence.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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