Research on feature matching algorithm of outdoor complex scenes

Yuzhou Cai; Renbo Zhang; Huilin Liu

doi:10.1117/12.2641844

10 November 2022 Research on feature matching algorithm of outdoor complex scenes

Yuzhou Cai, Renbo Zhang, Huilin Liu

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481Q (2022) https://doi.org/10.1117/12.2641844
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

An essential part of image processing is feature matching. The commonly used algorithms for feature matching include the scale-invariant feature transform (SIFT) algorithm, accelerated up robust features (surf), and oriented FAST and rotated BRIEF(ORB) algorithm. So far, most of the research on these algorithms at home and abroad mainly focuses on speed, accuracy and robustness, lacking specific analysis for specific scenes. To aim at three classical algorithms, this paper first uses the HPSequences Dataset to test speed and accuracy in general. Then, test the accuracy and speed with the light, angle, and ambiguity changing for a specific outdoor scene. The experimental results show that SURF can still maintain high matching accuracy when the object ambiguity and angle change. SIFT can still maintain high matching accuracy when the object's light changes. The robustness of ORB is the worst. In any case, the matching accuracy decreases the fastest.

Citation Download Citation

Yuzhou Cai, Renbo Zhang, and Huilin Liu "Research on feature matching algorithm of outdoor complex scenes", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481Q (10 November 2022); https://doi.org/10.1117/12.2641844

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