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Video Upright Adjustment and Stabilization

Title
Video Upright Adjustment and Stabilization
Authors
Jucheol Won
DGIST Authors
Won, Jucheol; Cho, Sunghyun; Chwa, Hoon Sung
Advisor(s)
좌훈승
Co-Advisor(s)
Sunghyun Cho
Issue Date
2020
Available Date
2020-06-23
Degree Date
2020-02
Type
Thesis
Description
Upright adjustment, Video stabilization, Camera path
Abstract
We propose a novel video upright adjustment method that can reliably correct slanted video contents that are often found in casual videos. Our approach combines deep learning and Bayesian inference to estimate accurate rotation angles from video frames. We train a convolutional neural network to obtain initial estimates of the rotation angles of input video frames. The initial estimates from the network are temporally inconsistent and inaccurate. To resolve this, we use Bayesian inference. We analyze estimation errors of the network, and derive an error model. We then use the error model to formulate video upright adjustment as a maximum a posteriori problem where we estimate consistent rotation angles from the initial estimates, while respecting relative rotations between consecutive frames. Finally, we propose a joint approach to video stabilization and upright adjustment, which minimizes information loss caused by separately handling stabilization and upright adjustment. Experimental results show that our video upright adjustment method can effectively correct slanted video contents, and its combination with video stabilization can achieve visually pleasing results from shaky and slanted videos.
Table Of Contents
I. INTRODUCTION 1.1. Related work II. ROTATION ESTIMATION NETWORK III. ERROR ANALYSIS IV. VIDEO UPRIGHT ADJUSTMENT 4.1. Initial angle estimation 4.2. Robust angle estimation 4.3. Optimization 4.4. Warping V. JOINT UPRIGHT ADJUSTMENT AND STABILIZATION 5.1. Bundled camera paths for video stabilization 5.2. Joint approach VI. EXPERIMENTS VII. CONCLUSION References
URI
http://dgist.dcollection.net/common/orgView/200000283400
http://hdl.handle.net/20.500.11750/12017
DOI
10.22677/Theses.200000283400
Degree
Master
Department
Information and Communication Engineering
University
DGIST
Related Researcher
  • Author Chwa, Hoon Sung Real-Time Computing Lab
  • Research Interests Real-Time Systems; Real-Time AI Services; Cyber-Physical Systems; Mobile Systems
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Department of Information and Communication EngineeringThesesMaster


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