Speeded Up Robust Features

This paper presents digital image stabilization with sub-image phase correlation based global motion estimation and Kalman filtering based motion correction. Global motion is estimated from the local motions of four sub-images each of which is detected using phase correlation based motion estimation. The global motion vector is decided according to the peak values of sub-image phase correlation surfaces, instead of impartial median filtering. The peak values of sub-image phase correlation surfaces reveal reliable local motion vectors, as poorly matched sub images result in considerably lower peaks in the phase correlation surface due to spread. The utilization of sub-images enables fast implementation of phase correlation based motion estimation. The global motion vectors of image frames are accumulated to obtain global displacement vectors, that are Kalman filtered for stabilization.

Digital image stabilization with sub-image phase correlation based global motion estimation

This paper presents a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.

SIFT Features Tracking for Video Stabilization

This paper proposes a new approach for video stabilization Most existing video stabilization methods adopt a framework of three steps, motion estimation, motion compensation and image composition Camera motion is often estimated based on pairwise registration between frames Thus, these methods often assume static scenes or distant backgrounds Furthermore, for scenes with moving objects, robust methods are required for finding the dominant motion Such assumptions and judgements could lead to errors in motion parameters Errors are compounded by motion compensation which smoothes motion parameters This paper proposes a method to directly stabilize a video without explicitly estimating camera motion, thus assuming neither motion models nor dominant motion The method first extracts robust feature trajectories from the input video Optimization is then performed to find a set of transformations to smooth out these trajectories and stabilize the video In addition, the optimization also considers quality of the stabilized video and selects a video with not only smooth camera motion but also less unfilled area after stabilization Experiments show that our method can deal with complicated videos containing near, large and multiple moving objects

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Video stabilization using robust feature trajectories

This paper presents a robust algorithm for video sequence stabilization. Motion estimation is achieved using block motion vectors. In this way, the motion estimator of the MPEG encoder can be used. The simple use of block motion vectors can give unreliable global motion vectors and so elaborations are done to make the algorithm robust.

Digital image stabilization by adaptive block motion vectors filtering

A fast digital image stabilizer based on the Gray-coded bit-plane matching is proposed which is robust to irregular conditions such as moving objects and intentional panning. The proposed digital image stabilization (DIS) system performs motion estimation using the Gray-coded bit-plane of video sequences, greatly reducing the computational load. This motion estimation method can be realized using only binary Boolean functions which have significantly reduced computational complexity, while the accuracy of motion estimation is maintained. In order to further improve the computational efficiency, the Gray-coded bit-plane matching with the three-step search (3SS) is proposed. Experimental results indicate that the proposed digital image stabilizer is a computationally efficient alternative to existing DIS systems.

Eui-Sung Kang

Papers

Fast digital image stabilizer based on Gray-coded bit-plane matching