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The inter-frame Feature Matching and Tracking of Binocular Vision Based on the ORB-PyrLK algorithm

机译:基于ORB-PYRLK算法的双目视觉互相特征匹配与跟踪

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The feature matching and tracking of binocular vision inter-frame images have been divided into three parts in this paper. Before image feature extraction, an image binarization segmentation method based on HSV color space is used to extract the target in order to reduce the searching range of feature points and improve the matching efficiency. About feature matching, a modified ORB method combining KNN rough algorithm and RANSAC fine algorithm is applied to improve the matching accuracy of left-right frame images. As to feature tracking which can be seen as the matching of front-rear frames, an inter-frame matching method based on ORB feature optical flow is proposed to realize the aim. Results are shown in several experiments to demonstrate that the proposed algorithm can improve the accuracy and efficiency of left-right frame and front-rear frame image matching.
机译:双目视觉帧间图像的特征匹配和跟踪已分为本文的三个部分。在图像特征提取之前,使用基于HSV颜色空间的图像二值化分割方法来提取目标,以减少特征点的搜索范围并提高匹配效率。关于特征匹配,应用了KNN粗算法和RANSAC精细算法的修改后的ORB方法,以提高左右帧图像的匹配精度。关于可以被视为前后帧的匹配的特征跟踪,提出了一种基于ORB特征光流的帧间匹配方法来实现目标。结果显示在几个实验中,以证明该算法可以提高左右框架和前后框架图像匹配的准确性和效率。

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