Evaluation of Image Feature Detection and Matching Algorithms

Abstract

Image features detection and matching algorithms play an important role in the field of machine vision. Among them, the computational efficiency and robust performance of the features detector descriptor selected by the algorithm have a great impact on the accuracy and time consumption of image matching. This paper comprehensively evaluates typical SIFT, SURF, ORB, BRISK, KAZE, AKAZE algorithms. The Oxford dataset is used to compare the robustness of various algorithms under illumination transformation, rotation transformation, scale transformation, blur transformation, and viewpoint transformation. Jitter video is also used to compare the anti-jitter ability for these algorithms. The indicators compared include: time of detecting features, time of matching images, total running time, number of detected feature points, accuracy, number of repeated feature points, and repetition rate. Experimental results show that, Under different transformations, each algorithm has its own advantages and disadvantages.