Circular Road Signs Research Articles

A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.

Method for Extracting Circular Road Signs on the Basis of Scene Image Features

Road signs provide important information to guide and regulate the behavior of drivers and pedestrians to make their journeys safer and more comfortable. Therefore, we have already proposed a method for extracting and recognizing circular road signs on the basis of the color information and shape features derived from a color scene image. In the previous method, the use of lightness in images and the determination of a dynamic threshold for the binary processes have not been achieved yet. This paper proposes an algorithm to extract circular road signs on the basis of features of scene images. That is, the proposed method makes good use of lightness as well as hue and chroma. Also, the thresholds regarding the chroma and lightness in each binary process of the blue road signs are able to be automatically decided using local features in the target image. Tests on 295 images suggest that the proposed method can accurately extract circular road signs.

Extracting circular road signs using specific color distinction and region limitation

There is now vigorous research on the intelligent transport system (ITS), and also on the advanced cruise-assist highway system (AHS), which is one of the ITS components. An element of the AHS is t...

Extracting circular road signs using specific color distinction and region limitation

AbstractThere is now vigorous research on the intelligent transport system (ITS), and also on the advanced cruise‐assist highway system (AHS), which is one of the ITS components. An element of the AHS is the system that extracts and recognizes road signs from color road images to alert the driver. This paper proposes a new technique that can extract circular road signs in a practical processing time. The proposed method first discriminates the color used in the road sign (specific color) from the input color road image. Then, the target region for extraction is limited on the basis of the color distribution, so that the processing time is drastically reduced. Furthermore, by using the area ratio of the specific color in the extraction region, the extraction accuracy is improved and the processing time is reduced. In evaluation experiment using 53 color road images, a high detection rate of 94.7% was achieved. The average processing time was approximately 2.18 seconds, which is close to practical. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(11): 90– 99, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10396

Circular road signs recognition with soft classifiers

In this paper a computer system for recognition of the circular prohibition and obligation road signs is proposed. Its main purpose is to assist a driver with information on passing signs, which in connection with an active cruise control can prevent

カラー情景画像における円形道路標識認識法

Road signs provide important information to guide and regulate the behavior of drivers and pedestrians in order to make their journeys safer and more comfortable.However,the shape features and color information displayed on circular road signs are not always the same.We propose a method for recognizing road signs on the basis of the shape features and color information derived from a color scene image.The proposed method works in three steps.First,a process already proposed in one of our previous studies detects road sign areas' color features and circularity using fuzzy reasoning.Second,the non-standard shape features of the detected road sign area are determined.Third,the values obtained from every sub-area of the image are used to enable the recognition of road signs.Our experimental results suggest that the proposed method can accurately recognize different types of circular road signs.

Information Extraction for Circular Road Signs in Color Scene Image

Information Extraction for Circular Road Signs in Color Scene Image

カラー情景画像における円形道路標識の内部領域推定

This paper describes an algorithm for estimation of internal area in the circular road signs from color scene image due to fuzzy reasoning. The algorithm works in three steps. First, regions of circular road sign in color scene image are detected. Secondly, color information is estimated due to fuzzy reasoning by using both color features and edge information. Thirdly, the resulting region is classified into three colors: red, blue and white. The experimental results indicate that the proposed algorithm is able to accurately estimate information of internal area in the circular road signs.

カラー情景画像を用いた円形道路標識の情報抽出

カラー情景画像を用いた円形道路標識の情報抽出

The solid angle subtended by a circular disc with application to roadsigns

The solid angle subtended by a disc at an arbitrary point is derived from the fundamental definition with the help of the computer algebra package MAPLE. The result is used to examine the apparent size of a circular roadsign when viewed from the road as a function of observation position, position and size of sign. Results are compared with rectangular and square signs of the same area and centre. It is found that rectangular signs have a larger apparent size than both square and circular signs which have very similar apparent sizes.