Abstract
A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements.
Highlights
As a new subject, machine vision technology has been gradually integrated into people’s lives in recent years
This paper re-distributes the brightness of the image by adopting the adaptive histogram equalization, reducing the influence of the light on the input picture, and the advantages and disadvantages are measured by the number of feature points, the number of matching points, and the running time
Algorithm and the Oriented Fast and Rotated Brief (ORB) algorithm based on image enhancement are compared respectively
Summary
Machine vision technology has been gradually integrated into people’s lives in recent years. Rublee et al improved the fast corner and brief descriptor and proposed an ORB feature that can effectively replace SIFT and SURF [8]. The ORB algorithm appropriately reduces the accuracy and robustness of feature points, improves the calculation speed, and reduces the calculation time, which is a good compromise between the quality and performance of different types of feature points [9]. Hong et al [20] matched the ORB feature point matching algorithm and eight parameters and combined the rotation model, improving the detection speed of the feature point; and. By comparing the logarithm of correct matching between the image features to be input and all template image features, a reasonable threshold is set, which effectively realizes the target classification of small samples. Oriented Fast and Rotated Brief (ORB) is based on the famous fast feature detection and brief feature descriptor
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