• 한국도로학회논문집
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• 제15권1호
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• pp.155-161
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• 2013

PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1375-1378
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• 1997

In this paper, we presents the algorithm which is to recognize the traffic sign on the road the traffic signal in a video image for autonomous navigation. First, the rocognition of traffic sign on the road can be detected using boundary point estimation form some scan-lines within the lane deducted. For this algorithm, index matrix method is used to detemine what sign is. Then, the traffic signal recognition is performed by usign the window minified by several scan-lines which position may be expected. For this algoritm, line profile concept is adopted.

• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• 대한인간공학회지
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• 제18권1호
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• pp.133-147
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• 1999

Traffic signs without a cognitive consideration in their design may cause information-processing problem that could result in a mental confusion among drivers often lead to a serious traffic accident. Therefore, in this study, several traffic signs currently used in Seoul Metropolitan area have been sampled and analyzed to identify design problems that usually caused by neglecting drivers cognitive ability. To classify the practical design problems, five major information-processing problems have been suggested: indistinguishable information, information conflict, missing information, sign-load mismatch, and information overload. In order to solve these cognitive problems, new traffic signs have been suggested in this study. An experiment was also performed to validate the new traffic sign. Twenty-four healthy subjects participated in the experiment. They were asked to answer the Question after observing the traffic signs continuously displayed on computer screen. The result indicated that subjects improved the accuracy in understanding the signs by 1.4 times when they used the suggested traffic sign compared to the old one. Based upon the results, a cognitive guideline was suggested for correct and speedy reading of traffic signs by improving information processing and reducing of human error. In conclusion, the traffic sign may well be applied to design an intelligent traffic sign system to increase the safety and comfort of drivers, especially in complex load condition.

• 한국전자통신학회논문지
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• 제17권1호
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• pp.105-110
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• 2022

교통 표지 인식은 교통 관련 문제를 해결하는 데 중요한 역할을 한다. 교통 표지 인식 및 분류 시스템은 교통안전, 교통 모니터링, 자율주행 서비스 및 자율주행 차의 핵심 구성 요소이다. 휴대용 장치에 적용할 수 있는 경량 모델은 설계 의제의 필수 측면이다. 우리는 교통 표지 인식 시스템을 위한 잔여 블록이 있는 경량 합성곱 신경망 모델을 제안한다. 제안된 모델은 공개적으로 사용 가능한 벤치마크 데이터에서 매우 경쟁력 있는 결과를 보여준다.

• 대한인간공학회지
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• 제21권3호
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• pp.25-34
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment the cognition time of the road sign in dynamic environment. This experiment was designed in with-subject design with two factors: vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods: the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2002년도 춘계학술대회논문집
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• pp.163-168
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment was designed in within-subject design with two factors; vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods; the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• 한국콘텐츠학회논문지
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• 제7권2호
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• pp.63-72
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• 2007

교통 표지판은 먼거리에서도 교통 표지라는 것을 쉽게 판별하여 단시간 내에 그 내용을 파악할 수 있어야 한다. 교통 표지판의 도로의 안전 주행에 있어 아주 중요한 객체로 도로 상의 다른 그 무엇보다도 먼저 인간의 시선을 잡아끌어야 한다. 이에 본 논문에서는 인간의 도로 상의 어떤 물체보다도 교통 표지판에 가장 먼저 시선을 집중한다는 가정하에 주의 모듈(Attention Module)을 사용하여 교통 표지판 영역을 추출하는 시스템을 제안하고자 한다. 특히 본 논문에서는 인간의 대상(object)인식과정, 특히 색상처리과정에서 어떠한 특징들이 사용되어지는지를 기존의 정신물리학적, 생리학적 실험결과를 통해 분석하였고, 이 분석결과를 통해 얻어진 특징들을 사용하여 교통 표지판 영역을 추출하였다. 실제 도로위에서 찍은 실영상을 대상으로 실험하였으며, 실험을 통하여 평균 97.8%의 탐지율을 보임을 확인하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2015년도 하계학술대회
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• pp.516-517
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• 2015

The automatic traffic sign detection and recognition has been one of the highly researched and an important component of advanced driver assistance systems (ADAS). They are designed especially to warn the drivers of imminent dangers such as sharp curves, under construction zone, etc. This paper presents a traffic sign recognition (TSR) system using morphological operations and multiple descriptors. The TSR system is realized in three stages: segmentation, shape classification and recognition stage. The system is designed to attain maximum accuracy at the segmentation stage with the inclusion of morphological operations and boost the computation time at the shape classification stage using MB-LBP descriptor. The proposed system is tested on the German traffic sign recognition benchmark (GTSRB) and on Korean traffic sign dataset.