Improved ORB matching algorithm based on adaptive threshold

Abstract

Aiming at the problem of ORB feature matching algorithm extracting background pixels as feature points and matching wrong feature points in a complex background environment, an improved ORB algorithm based on adaptive threshold is proposed, and GMS algorithm is used to screen out mismatches in the feature matching stage. First, the algorithm calculates the mean and standard deviation of the image to be matched and the reference image. Then it inputs the obtained data into the adaptive threshold calculation stage to obtain the adaptive threshold. Finally it inputs the adaptive threshold into the feature extraction and matching stage. The experimental results show that the improved ORB algorithm reduces the number of features extracted from the background of the exhibits in the complex environment of the museum, and the matching algorithm combined with the ORB algorithm and GMS increases the correct matching on the basis of slightly shorter time than the original algorithm. The algorithm has strong robustness and real-time performance.