Abstract
This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using Hue, Saturation and Value (HSV) color spaces for extracting the speed limit numbers on the identified speed-limit signs, and finally (3) a method for recognition of the extracted numbers using a neural network. The method of traffic sign recognition previously proposed by the author consisted of extracting geometric shapes from the sign and recognizing them based on their aspect ratios. This method cannot be used for the numbers on speed-limit signs because the numbers all have the same aspect ratios. In a study that proposed recognition of speed limit numbers using an Eigen space method, a method using only color information was used to detect speed-limit signs from images of scenery. Because this method used only color information for detection, precise color information settings and processing to exclude everything other than the signs are necessary in an environment where many colors similar to the speed-limit signs exist, and further study of the method for sign detection is needed. This study focuses on considering the following three points. (1) Make it possible to detect only the speed-limit sign in an image of scenery using a single process focusing on the local patterns of speed limit signs. (2) Make it possible to separate and extract the two-digit numbers on a speed-limit sign in cases when the two-digit numbers are incorrectly extracted as a single area due to the light environment. (3) Make it possible to identify the numbers using a neural network by focusing on three feature quantities. This study also used the proposed method with still images in order to validate it.
Highlights
As one part of research related to Intelligent Transport Systems (ITS), in Japan, much research has been conducted regarding Advanced cruise-assist Highway Systems (AHS), aiming to ensure the safety and smoothness of automobile driving [1,2,3,4,5,6,7,8,9]
The use of neural networks as a method of machine learning accordance with rules, and identifies the class of the observed pattern by judging which of the class based on statistical processing makes it relatively simple to configure a classifier with good rules were used to generate it
4a, aonspeed limit sign consists ofthe a red circle on the periphery with the numbers numbers from the blue area, the speed limit sign extracted from the color image is converted to a printed in blue on a white background inside the circle
Summary
As one part of research related to Intelligent Transport Systems (ITS), in Japan, much research has been conducted regarding Advanced cruise-assist Highway Systems (AHS), aiming to ensure the safety and smoothness of automobile driving [1,2,3,4,5,6,7,8,9]. In consideration of the fact that excessive speed is a major cause of fatal accidents, and because recognition of the speed-limit signs that play a major role in preventing fatal accidents and other serious accidents is more important, a speed-limit sign recognition method utilizing an Eigen space method based on the KL transform was proposed [17] This method yielded a fast processing speed, was able to detect the targets without fail, and was robust in response to geometrical deformation inin the surrounding environment or deformation of ofthe thesign signimage imageresulting resultingfrom fromchanges changes the surrounding environment the shortening distance between the sign and and vehicle.
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