Research on images feature matching model based on similarity of ORB features

At present, objectives matching has become an essential method in face recognitions, images stitching and autonomous driving. Existing objectives matching process is almost relied on the scale invariant transform, which ignores the flexibility information of ORB (Oriented Fast and Rotated Brief) features. In this work, we propose a novel objective matching method by deploying an improved ORB features extraction module and decrease the computation costs. Specifically, we detect the features in multiple scales of input images and utilize the features from accelerated segment algorithm to extract the ORB features, which can guarantee the uniform distribution and scale invariant characteristic of extracted features. Subsequently, we adopt the similarity algorithm to match the extracted features and novel images to achieve the objective matching. From our extensive experiment and analysis, we can significantly observe that our model achieves the highest matching accuracy with acceptable computation costs through comparing with existing methods.