Benchmarking of Feature Detectors and Matchers using OpenCV-Python Wrapper - Golovnin, Oleg; Rybnikov, Dmitry (2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021)
This paper proposes a comparative analysis of AKAZE, BRISK, KAZE, ORB, and SIFT features detecting algorithms in combination with BF and FLANN feature matching algorithms. The comparative evaluation was implemented using the OpenCV-Python wrapper. In the first phase, for each pair of algorithms, we estimated the time of detection, the number of detected features, the time of matching, and the number of matched features. In the second phase, we estimated the time of feature detection depending on image resolution and the number of features. The results showed the best summary efficiency is achieved when using a pair of ORBFLANN algorithms. The performance of detectors and matchers is determined only by hardware features, image sets, and configuration while using the OpenCV-Python wrapper does not affect performance.