• Journal of Korean Society for Geospatial Information Science
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• v.6 no.2 s.12
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• pp.35-44
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• 1998

Road guide sign is one of important traffic facility, which guides a driver a direction to go. A road is being newly built and/or an existing road can be temporarily closed very often at metropolitan area. Such information has to be linked to a road guide sign and to be updated in time. Otherwise, it might cause not only confusion to a driver but also traffic accident and/or traffic congestion. However, road guide signs are not properly managed at this time since the amounts of information are huge and they are being managed through paper maps manually. A decision-making framework has been developed to manage a variety of information of road guide signs based on the concept of GIS(Geographic Information Systems). Especially, it is designed on the fact that the existing national digital base maps under the NGIS(National Geographic Information Systems) program can be utilized. This framework has several functions such as the manipulation of statistics, landmark linkages along the selected traffic corridor and automatic design of signboard's content. This system is applied to the Kangnam district in Seoul Metropolitan area (SMA) for a case study.

• Journal of IKEEE
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• v.16 no.4
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• pp.356-363
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• 2012

Information technology improves convenience, safety, and performance of automobiles. Recently, a lot of algorithms are studied to provide safety and environment information for driving, and traffic sign recognition is one of them. It can provide important information for safety driving. In this paper, we propose a method for traffic sign detection and identification concentrating on reducing the computation time. First, potential traffic signs are segmented by color threshold, and a polygon approximation algorithm is used to detect appropriate polygons. The potential signs are compared with the template signs in the database using SURF and ORB feature matching method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.471-476
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• 2004

This paper presents an active vision system for on-line traffic sign recognition. The system is composed of two cameras, one is equipped with a wide-angle lens and the other with a telephoto lends, and a PC with an image processing board. The system first detects candidates for traffic signs in the wide-angle image using color, intensity, and shape information. For each candidate, the telephoto-camera is directed to its predicted position to capture the candidate in a large size in the image. The recognition algorithm is designed by intensively using built in functions of an off-the-shelf image processing board to realize both easy implementation and fast recognition. The results of on-road experiments show the feasibility of the system.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.149-158
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• 2004

This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.81-84
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• 2003

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.117-132
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• 2021

Due to the terrain in Korea, there are many road sections passing through mountainous areas. During the winter, there is a higher risk of traffic accidents, due to black ice caused by the lack of sunlight. Despite domestic road freezing safety measures, accidents caused by road freezing results in severe traffic accidents. Under these considerations, this study analyzed whether traffic safety signs that change in response to the external temperature help drivers recognize frozen road segments. The study was conducted through analysis of the effect of the signs on a driver's perspective. For the signs under development, out of the signs designed by experts, the sign design which received the highest visibility and effectiveness evaluation ratings from the general public was selected. The sign was implemented through Virtual Reality (VR) and installed on the right side of the road to analyze the effect on gazing and driving behavior. As a result of analyzing the driver's driving behavior, a speed reduction of about 7km/h or more was found in the sign section. Therefore, It was found that the existence of the sign had a strong relationship with the rate of the drivers' speed reduction.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.55-69
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• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.504-506
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• 2012

운전자 지원 시스템(ADAS)은 최근 지능형 자동차 분야에서 중요한 이슈로 손꼽히는 기술 중 하나이다. 이 중에서 실시간 표지판 인식 기술은 운전자 지원 시스템의 하나로 운전자의 안전과 직결될 수 있어 높은 정확성과 실시간성이 요구된다. 그동안 표지판 인식 분야는 색상과 현상을 기반으로 연구가 진행되어왔으나, 교통 표지판은 국가별로 그 특징과 형태가 각기 상이하여 적용하는데 한계가 있다. 본 논문에서는 한국의 속도제한 표지판을 실시간으로 검출하고 인식하기 위하여, 1) 영상에서 색상 특징을 이용하여 후보 영역을 검출하고, 2) 형상 정보를 분석하여 표지판의 형태를 검증하고, 3) 검출된 후보영역의 내부문자(숫자)를 분할하고 인식하는 시스템을 제안한다.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.141-144
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• 2000

본 논문은 교통 안전 표지판 인식에 적용이 가능한 새로운 영상의 특징 정보 추출 방법을 제안한다. 제안된 방법은 인간의 인식 시스템에서 이용하는 기본 특징인 색과 형태정보를 이용하여 영상 인식에 적용한다. 색 정보의 추출은 RGB성분의 히스토그램 분포를 이용하고 형태 정보의 추출은 기하학적인 형태 정보 추출 방법과 모멘트를 이용한다. 본 논문은 유사 영상 검색을 위한 새로운 특징 정보 추출 방법과 간단한 특징 정보 표현 그리고 계산량의 감소 효과를 얻었다.