摘要
小型无人机电子稳像可以纠正视频影像中存在的晃动、震动、畸变等不稳定因素,有利于视频影像目标跟踪、精确打击,同时还能缓解观察者的视觉疲劳。电子稳像有3个主要的步骤:全局运动估计、主运动估计和运动补偿。本文主要对主运动估计的方法进行研究,并提出了一种快速最优化的稳像方法,该方法能够快速准确的估计小型无人机视频影像的主运动。该方法结合了L1最优化方法的精确性和Kalman滤波方法处理速度快的优点,并克服Kalman滤波法预测精度不高和L1最优化方法只能事后处理的问题,从而获到更好的稳像效果。最后采用实际飞行数据对本文提出算法进行了验证,结果表明,本文所提出的方法在保证稳像处理精度的前提下,也保证了处理效率。
The electronic image stabilization of Unmanned Air Vehicle（UAV）video can correct the shaking motions,disorienting rotations,noisy and distorted images and other unwanted effects.It will benefit the target tracking and precision strike,and also help observers to obtain useful information.Electronic video stabilization generally contains three main steps,global motion analysis,intentional motion estimation and motion compensation.This paper mainly researches the intentional motion estimation,and presents a method of fast optimal video stabilization.This method can estimate the intentional motion of UAV video fast and accurately.This method combine the accuracy in L1 optimization method and the fast speed in Kalman filtering method,solve the hysteresis problem of Kalman filtering method and the unreal-time processing problem of L1 optimization,and achieve the better effect.At last,the method proposed in this paper was carried out and validated by actual airborne videos.The results show that the proposed method can improve the accuracy of Kalman filtering method,and its efficiency is higher than L1 optimization method.
出处
《影像科学与光化学》
CAS
CSCD
北大核心
2016年第1期51-58,共8页
Imaging Science and Photochemistry
关键词
小型无人机
电子稳像
主运动估计
L1最优化
KALMAN滤波
small Unmanned Air Vehicle（UAV）
electronic video stabilization
intentional motion estimation
L1optimization
Kalman filtering method
