基于关键点及光流的平面物体跟踪算法  

Planar Object Tracking Algorithm Based on Key Points and Optical Flow

作　　者：季皓宣烨 梁鹏鹏[1] 柴玉梅[1] 王黎明[1] JI Haoxuanye;LIANG Pengpeng;CHAI Yumei;WANG Liming(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学信息工程学院,郑州450001

出　　处：《计算机工程》2021年第4期234-240,共7页Computer Engineering

基　　金：国家自然科学基金(61806181)。

摘　　要：为提高复杂场景下基于关键点的平面物体跟踪算法的鲁棒性,提出一种融合光流的平面物体跟踪算法。检测目标物体与输入图像的关键点及其对应描述符,由最近邻匹配方法构建目标与图像间关键点匹配集合,通过光流法构建相邻两张图像间关键点的对应关系,将已构建的关键点匹配集合与基于光流的对应关系通过加权平均的策略进行融合,得出修正的关键点匹配集合,根据关键点匹配估计目标物体在当前图像的单应性变换矩阵,从而完成目标跟踪。在POT数据集上的实验结果表明,与SIFT、FERNS等算法相比,在校正误差阈值为5时,该算法在所有图像序列上的平均跟踪精度达到66.67%,具有较好的跟踪性能。To improve the robustness of the planar object tracking algorithm based on key point in complex scenes,a planar object tracking algorithm based on key point using optical flow is proposed.The key points and their corresponding descriptors of the target and the input image are detected,and the nearest neighbor matching method is used to construct the key point matching set for the target and the image.Then optical flow is adopted to construct the correspondences between key points of the two adjacent frames.The constructed key point matching set and the optical flow-based correspondences are fused with a weighted average strategy to obtain a refined key point matching set.Finally,the target is tracked by estimating the homography transformation between the target and the current image based on the key point matching result.Experimental results on the POT dataset show that when the alignment error threshold is 5,the average tracking precision of the proposed algorithm reaches 66.67%for all image sequences,outperforming SIFT,FERNS and other algorithms.

关 键 词：平面物体跟踪 关键点 光流法 惩罚系数 单应性变换 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]