Video Stabilization
22 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
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.
DUT: Learning Video Stabilization by Simply Watching Unstable Videos
In this paper, we attempt to tackle the video stabilization problem in a deep unsupervised learning manner, which borrows the divide-and-conquer idea from traditional stabilizers while leveraging the representation power of DNNs to handle the challenges in real-world scenarios.
Hybrid Neural Fusion for Full-frame Video Stabilization
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
Robust Gyroscope-Aided Camera Self-Calibration
This application paper proposes a model for estimating the parameters on the fly by fusing gyroscope and camera data, both readily available in modern day smartphones.
Deep Iterative Frame Interpolation for Full-frame Video Stabilization
We present a novel deep approach to video stabilization which can generate video frames without cropping and low distortion.
Real-Time Selfie Video Stabilization
Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods.
Deep Motion Blind Video Stabilization
Despite the advances in the field of generative models in computer vision, video stabilization still lacks a pure regressive deep-learning-based formulation.
Deep Online Fused Video Stabilization
We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning.
A Method for Detection of Small Moving Objects in UAV Videos
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.
Good Practices and A Strong Baseline for Traffic Anomaly Detection
In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing.