中文摘要：

运动恢复结构（SFM）是指通过分析二维图像序列恢复三维结构信息的过程,在计算机视觉的多种应用中起着重要的作用。特征跟踪是大规模SFM的核心组成部分,但现有的多视图特征跟踪算法在鲁棒性和效率上还存在不足,为解决这一问题,提出了一种快速和鲁棒的特征跟踪（FRFT）算法。首先,采用AGAST进行特征点检测,并使用图像矩为AGAST特征定义主方向,为构造旋转不变的描述子奠定基础;其次,在差分高斯金字塔空间内,根据中心点与邻域像素之间的差值构造特征描述子,避免光照和尺度变化对特征匹配的影响;再次,为了提高特征匹配效率,对特征集合进行聚类,采用KD-Tree加速特征匹配,提高算法的时间效率;最后,采用4种方式对FRFT算法进行验证,并与现有经典算法进行比较。实验结果表明,FRFT算法在鲁棒性和时间效率方面均优于现有经典算法。

英文摘要：

Structure from motion（ SFM） refers to a process in which the 3D structure is created by analyzing 2D image sequences,which is very important in many applications of computer vision. Feature tracking is one of the core components of large-scale SFM. However,the robustness and time efficiency of the existing algorithms are needed to be improved. To address these issues,a fast and robust feature tracking（ FRFT） algorithm is presented. Firstly,images moments are used to define a main direction for AGAST feature point,which can help to construct a rotation invariance descriptor. Secondly,in the space of the difference of Gaussian,the difference between the center point and its neighbor points is used to construct a descriptor for the OAGAST keypoint,which can avoid the influence of illumination and scale change on the feature matching. Thirdly,to improve the time efficiency of feature matching,the large feature set is clustered to some small ones,and KD-Tree is used to accelerate feature matching for improving the time efficiency of FRFT. Finally,the proposed method is evaluated with four ways,and compared with the state-of-the-art methods. Experimental results show that the proposed FRFT method outperforms the state-of-the art ones on robustness and time efficiency.