12 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Video Stabilization
Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects
The emergence of low-cost high-quality personal wearable cameras combined with the increasing storage capacity of video-sharing websites have evoked a growing interest in first-person videos, since most videos are composed of long-running unedited streams which are usually tedious and unpleasant to watch.
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
We present a novel deep approach to video stabilization which can generate video frames without cropping and low distortion.
To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds.
In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing.
Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration.