摘要
提出一种基于运动分类的全局运动估计算法.首先,分区提取图像中的鲁棒Harris特征点,并采用特征窗匹配思路,提高匹配速度;其次,对运动矢量在平移、旋转和缩放模式下的统计特性进行分析,提出运动类型快速判定方法,并验证特征点的有效性;再次,将有效点对代入运动方程,求取全局运动参数;最后,结合Kalman滤波来补偿当前帧实现视频稳像.实验结果表明,该算法能够处理含摄像机扫描和抖动的复杂场景,检测误差小于0.5像素,且达到实时处理.
A global motion estimation algorithm based on motion classification is presented. Firstly, the Harris feature points are selected evenly and matched by using feature window. Hence, the statistic features of all motions are analyzed according to different motion kinds including translation, rotation and zoom. Then, the fast motion classification method is proposed to validate all points. Thirdly, the remained global feature points are brought to the affine model to compute global motion. Finally, the Kalman filter is used to compensate each current frame. Experimental results show that the algorithm can correctly detect global motion in dynamic scenes with camera scan and various dithering. The estimation error is below 1/2 pixel at real-time stabilization, which can greatly improve the stability and fidelity of videos.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第10期1575-1578,1583,共5页
Control and Decision
基金
国家自然科学青年基金项目(61003196)
高校基本科研业务费专项资助项目(K50510040004)
关键词
电子稳像
全局运动估计
运动补偿
特征点
electronic image stabilization
global motion estimation
motion compensation
feature points
