航拍视频拼图中基于特征匹配的全局运动估计方法

Feature Matching Based Global Motion Estimation in Aerial Video Mosaicing

主要研究了航拍视频拼图中的全局运动估计问题。根据航拍视频成像过程的特点,得出相邻帧之间全局运动的4参数近似变换模型,该模型参数较少,鲁棒性好且计算简单,任意帧之间的变换模型通过"帧到帧"的方式递归获得。提出了一种双向最近邻距离比的尺度不变特征(SIFT)的匹配方法,提取出有效的匹配点,并采用随机采样一致性(RANSAC)方法去除外点和估计全局运动参数。从试验结果可以看出,该方法估计出的全局运动参数精度高,鲁棒性好。

Global motion estimation is a critical problem in aerial video mosaicing. A more simple and robust linear similarity transformation model with only 4 degrees of freedom is proposed to satisfy the relationship between two consecutive frames. And the transformation between two non-contiguous frames can be obtained by frame-to-frame recursion. Feature-matching-based approach is adopted for image registration. Once scale invariant feature transform （SIFT） features extracted, they are matched by a novel matching scheme--bidirectional nearest neighbor distance ratio. Finally, the motion parameters can be estimated by random sample consensus （RANSAC） algorithm. The experimental results show the high accuracy of the global motion model for aerial video mosaicing and the good performance of the proposed matching scheme.