摘要
转动抖动补偿是视频稳像的难点,针对转动抖动补偿中的关键技术特征点的筛选与匹配展开研究。建立了图像的6参数仿射模型;推导得到估计有意运动参数的超定方程;采用最小二乘迭代算法来去除绝对误差和(SAD)算法误判的特征点;采用金字塔(LK)光流算法来对旋转视频进行特征点匹配。编程实现算法;用特征窗口梯度矩阵法(KLT)提取特征后,分别用SAD算法和LK光流算法进行匹配,求解得到旋转变换阵参数误差,分析、比较并图示了误差原因;利用Kalman滤波去除无意运动;对含转动抖动的视频进行稳像补偿。在自主移动机器人平台上开展了实验。结果表明LK光流算法相比SAD算法对旋转视频的特征点匹配误差小,结合Kalman滤波可有效补偿转动抖动,将最大8.37°的转动抖动稳像到3.68°以下。
Rotary jitter compensation is a difficulty in video image stabilization. The feature point matc- hing and inaccurate point filtering were studied. An affine model with 6 parameters was established for moving images, and an over-determined equation to estimate motion parameters was derived. The least squares iterative algorithm was used to remove error feature points judged by sum of absolute difference （SAD） matching algorithm. A pyramid-style Lucas-Kanade （LK） algorithm based on optical flow was a- dopted for feature point matching of rotary video. All algorithms were programmed. After detecting feature points by using the gradient matrix of the feature window （KLT） method, SAD and LK algorithms were used to match feature points respectively, and the rotation matrix parameter errors were obtained and compared. The reasons of matching error were analyzed. Kalman filter was used to smooth rotation param- eters. All algorithms were implemented on an autonomous robot. The experiment results show that LK can get less matching errors than SAD for rotation jitter, and Kalman filter makes the maximum 8.37~ rotation jitter less than 3.68~, thus it can compensate the rotary video effectively.
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2012年第11期1329-1334,共6页
Acta Armamentarii
基金
国家自然科学基金项目(50905170)
浙江省自然科学基金项目(Y1090042)
关键词
信息处理技术
特征点
光流
抖动补偿
机器人
information processing
feature point
optical flow
jitter compensation
robot
