Car model recognition from frontal image using BRISK

Malisa Huzaifa, I. Suwardi
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引用次数: 1

Abstract

Car Recognition is a part of Intelligent Transportation System. This research proposes the manufacture of ITS-based system to identify car model from Its frontal image using Binary Robust Invariant Scalable method. The BRISK method is used to detect image keypoint, and it uses Hamming Distance for keypoint matching. As for matching error, this research depends on RANSAC. BRISK method excellence lies in the speed of scale space searching of images. Car model recognition using BRISK has an accuracy rate of 96.25%.
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基于正面图像的汽车模型识别
汽车识别是智能交通系统的一部分。本研究提出利用二值鲁棒不变可扩展方法制作基于Its的汽车正面图像模型识别系统。采用轻快方法检测图像关键点,并利用汉明距离进行关键点匹配。对于匹配误差,本研究依赖RANSAC。BRISK方法的优点在于对图像的尺度空间搜索速度快。使用BRISK进行汽车模型识别,准确率达到96.25%。
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