Comparison of OpenCV's feature detectors and feature matchers

Abstract

There exists a range of feature detecting and feature matching algorithms; many of which have been included in the Open Computer Vision (OpenCV) library. However, given these different tools, which one should be used? This paper discusses the implementation and comparison of a range of the library's feature detectors and feature matchers. It shows that the Speeded-Up Robust Features (SURF) detector found the greatest number of features in an image, and that the Brute Force (BF) matcher matched the greatest number of detected features in an image pair. Given a benchmark image set, OpenCV's SURF detector found, on average, 1907.20 features in 1538.61 ms, and OpenCV's BF matcher, on average, matched features in 160.24 ms. The combination of the Binary Robust Invariant Scalable Key-points (BRISK) detector and BF matcher was found to be the highest ranked combination of OpenCV's feature detectors and feature matchers; on average, detecting and matching 1132.00 and 80.20 features, respectively, in 265.67 ms. It was concluded that if the number of features detected is important, the SURF detector should be used; else, if the number of features matched is important, the BF matcher should be used; otherwise, the combination of the OpenCV's BRISK feature detector and BF feature matcher should be used.