Two-Pass K Nearest Neighbor Search for Feature Tracking

IEEE Access

Wei Jia¹  Mingwei Cao¹  Wenjun Xie¹  Xiaoping Liu¹  Liping Zheng¹  Zhihan Lv² 

[1] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China;School of Data Science and Software Engineering, Qingdao University, Qingdao, China;

关键词: Feature tracking;    K nearest neighbor;    3D reconstruction;    structure from motion;    feature matching;   

DOI  :  10.1109/ACCESS.2018.2879337

来源: DOAJ

【 摘 要 】

In recent years, feature tracking has become one of the most important research topics in computer vision. Many efforts have been made to design excellent feature matching methods. For large-scale structure from motion, however, existing feature tracking methods still need to improve in aspects of speed and matching confidence. To defense the drawbacks, in this paper, we design a simple and efficient feature tracking method based on the standard $k$ nearest neighbor search. First, the parallel scale-invariant feature transform (SIFT) is selected as the feature detector to locate keypoints. Second, the principal component analysis-based SIFT-descriptor extractor is used to compute robust descriptions for the selected keypoints, in which normalized operation is used for boosting the matching score. Third, the two-pass $k$ nearest neighbor search (TP-KNN) is proposed to produce correspondences for image pairs, then leading a significant improvement in the number of matches. Moreover, a geometry-constraint approach is proposed to remove outliers from the initial matches for boosting the matching precision. Finally, we conduct experiment on several challenging benchmark datasets to assess the TP-KNN method against the state-of-the-art methods. Experimental results indicate that the TP-KNN has the best performance in both speed and accuracy.

【 授权许可】

Unknown