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.
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