移动通信2024，Vol.48Issue(11)：86-91,129,7.DOI:10.3969/j.issn.1006-1010.20240925-0002
基于深度学习的水面深度视频稳像
Deep Learning-Based Video Stabilization for Water Surface Scenes
摘要
Abstract
Video stabilization and deep video stabilization for water surface scenes have significant applications in fields such as ocean monitoring,fisheries resource surveys,and live streaming of water sports.This study applies deep learning to achieve deep video stabilization for water surface scenes.Initially,Adobe Premiere Pro's Warp Stabilizer was used to stabilize water surface videos,reducing horizontal drone shake in top-down water surface footage.Metrics such as cropping ratio,distortion value,and cumulative optical flow were employed for evaluation.Results showed minimal quality loss post-stabilization,with reduced inter-frame differences compared to the original video,indicating improved video stability.Subsequently,a neural deep video stabilization method was applied,incorporating a depth predictor and a stabilization network.Experimental results demonstrate that after deep stabilization,boundaries between water waves and the background became sharper,textures were clearer,and boundary contours closely matched those of the original video,achieving excellent deep stabilization performance.关键词
视频稳像/深度视频稳像/稳定性评估/水面场景Key words
video stabilization/deep video stabilization/stability assessment/water surface scene分类
信息技术与安全科学引用本文复制引用
杨礼华,陈泽钰,施俊杰,潘海朗,杨劲松,LIU Jianguo..基于深度学习的水面深度视频稳像[J].移动通信,2024,48(11):86-91,129,7.基金项目
国家自然科学基金"近岸条件下北斗导航反射信号海面风速反演能力增强机制研究"(42306200) （42306200）
国家自然科学基金青年科学基金项目"基于深度学习的热带气旋风场重构及风暴潮模拟"(42306216) （42306216）
国家重点研发计划项目"环境遥感巡查与立体智能感知"(2022YFC3103101) （2022YFC3103101）
