• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1318-1320
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• 2013

본 논문은, 도로표지 영상의 자동 획득, 관련 정보의 검출/인식 및 온라인 갱신 기능을 갖는 차세대 도로표지 관리시스템에 요구되는, 도로표지 영상으로부터 방향지시 정보과 국문지명을 검출하는 방법을 제안한다. 자획 폭 변환을 사용하여 비텍스트 정보를 구분하고, 한글 템플릿 매칭을 사용하여 국문 지명의 영역을 검출한다. 제안한 방법은 실제 도로표지 영상에 대해서 98%의 방향지시 정보 검출율과 92%의 국문지명 검출율 성능을 보였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.28-29
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• 2012

IT 및 자동차 관련 기술의 융합 기술의 발전에 따라 자동차의 안전 및 운전편의 정보 제공에 대한 관심이 높아지면서 인간의 시각 및 지각의 한계를 보완해 줄 수 있는 보조 도구들에 대한 연구 및 개발이 활발히 이루어지고 있다. 그러나 기존의 컴퓨터 비전 기반 도로 교통 표지판 자동 검출 및 인식 기술들은 센싱 장비들의 가격 또는 조도와 원근감에 따른 교통 표지판의 색상과 모양 왜곡으로 인해 해당 표지판들의 검출을 어렵게 한다. 따라서 본 논문에서는 차량 내 탑재가 가능한 저가의 비전 카메라를 이용하여 교통표지판 칼라 분석 및 원근 보정을 통해 운전자에게 효과적으로 도로표지판 정보를 제공할 수 있는 검출기법을 제안한다. 실험을 통해 도로주행영상 내 도로표지판들을 효과적으로 검출할 수 있음을 확인하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.91-98
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• 2011

Road signs are part of the traffic facilities intended to guide drivers to their destinations in a safe and comfortable manner. Due to the creation of new routes, changes to the old routes, and the deterioration of road signs, road signs do require efforts to do ongoing field investigations and put the results in a database. The purpose of this study was to propose methodologies to do field investigations and build a database for road signs efficiently. For that purpose, a mobile mapping system was designed for field investigations. The designed mobile mapping system was comprised of three cameras to produce image information about road signs, GPS/IMU/DMI to obtain information about the position and attitude of a vehicle, and a laser scanner to generate information about the locations of road signs and routes. Also proposed in the study was a procedure to automatically detect the areas of road signs in the road signs images and recognize their characters.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.513-515
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• 2015

최근 지능형 차량과 ADAS(Advanced Driver Assistance System) 개발에 있어 차량 영상을 이용한 도로 정보 분석이 중요한 화두로 떠오르고 있다. 다양한 도로 정보 중에서 도로표지판 검출 및 판단은 차량 운행 환경을 파악할 수 있는 중요한 과정이 될 수 있다. 이에 본 논문에서는 차량 영상에서의 색상 정보를 이용하여 표지판의 후보 영역을 추출(Candidate Generation)하고, 후보 영상에 대한 HOG(Histogram of Gradient) 특징 분석을 통해 도로표지판 여부와 그 종류를 판단(Object Classification)하는 알고리즘을 구현하였다. 또한 구현 알고리즘은 실시간 처리가 가능한 속도를 보여주어 지능형 차량 또는 ADAS에서의 실제 적용이 가능하도록 하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.1-9
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• 2014

Recently, a study is conducting to image collection and auto detection of attribute information using mobile mapping system. The road sign attribute information detection is difficult because of various size and placement, interference of other facilities like trees. In this study, a text detection method that does not rely on a Korean character template is required to successfully detect the target text when a variety of differently sized texts are present near the target texts. To overcome this, the method of incremental right-to-left blob projection (IRBP) was suggested as a solution; the potential and improvement of the method was also assessed. To assess the performance improvement of the IRBP that was developed, the IRBP method was compared to the existing method that uses Korean templates through the 60 videos of street signs that were used. It was verified that text detection can be improved with the IRBP method.

• Journal of KIISE
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• v.42 no.2
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• pp.203-212
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• 2015

This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.430-443
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• 2013

The recognition of current position and road surrounding of car is very important to driver for safe driving. This paper presents the recognition technique of the road traveling environment using position information hiding and viewpoint transform that monitors the information of road furniture and signature and notifies them to driver. The proposed scheme generates the road images into which the position information are hided, from car camera and GPS module and provides the road information to driver through the viewpoint transformation and the road signature detection. The driving tests with camera and GPS module verified that the position information hiding takes about 66.5ms per frame, the detection rate of road signature is about 95.83%, and the road signature detection takes about 227.45ms per frame. Therefore, we know that the proposed scheme can recognize the road traveling environment on the road video with 15 frame rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.41-51
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• 2010

In the research for driver assistance systems, traffic sign information to the driver must be a very important information. Therefore, the detection system of traffic signs located on the road should be able to handel real-time. To detect the traffic signs, color and shape of traffic signs is to use the information after images obtained using the CCD camera. In the road environment, however, using color information to detect traffic sings will cause many problems due to changes of weather and environmental factors. In this paper, to solve it, the candidate traffic sign regions are detected from road images obtained in a variety of the illumination changes using the HSI eign-color model. And then, using the invariant moment-based SVM classifier to detect traffic signs are proposed. Experimental results show that, traffic sign detection rate is 91%, and the processing time per frame is 0.38sec. Proposed method is useful for real-time intelligent traffic guidance systems can be applied.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.19-25
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• 2013

Recently, more accurate database information of facilities is being required, with the increase in importance of urban road facility management. Therefore, this study proposed how to automatically detect particular traffic signs necessary for efficient construction of road facility DB. For this study, central locations of facilities were searched, after recognition and automatic detection of particular traffic signs through an image. Then, coordinate values of traffic signs calculated in the study were compared with real coordinate values, in order to evaluate the accuracy of traffic sign locations which were finally detected. Computer vision technology was used in recognizing and detecting traffic signs through OPEN CV-based coding, and photogrammetry was used in calculating accurate locations of detected traffic signs. For the experiment, circular road signal(No Parking) and triangular road signal(Crosswalk) were chosen out of various kinds of road signals. The research result showed that the circular road signal had a nearly 50cm error value, and the triangular road signal had a nearly 60cm error value, when comparing the calculated coordinates with the real coordinates. Though this result is not satisfactory, it is considered that there would be no problem to find locations of traffic signs.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.361-363
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• 2016

현재 차량 내 운전자에게 편의성과 안전성을 제공하는 시스템이 활발히 개발 중이고 향후 ADAS(Advanced Driver Assistance System)와 스마트 자동차에서 영상 정보를 이용한 물체 추적과 분석은 매우 중요한 부분을 차지하고 있다. 영상에서 얻을 수 있는 정보 중 현재 도로의 이정표 정보는 중요한 분석 정보로 사용된다. 하지만 국내 도로표지판 검출 연구의 경우 유럽과 북미와 비교하여 연구 개발이 활발히 진행되고 있지 않다. 국내의 경우 도로 이정표에서 영문자뿐만 아니라 한글 문자 정보까지 포함하고 있어 검출이 쉽지 않다. 또한 비교적 밝고 잡음이 적은 검출하기 좋은 환경에서는 검출이 잘 되지만 명암이 뚜렷하지 않고 잡음이 많은 환경에서는 도로 이정표 문자 검출이 어렵다. 이에 본 논문에서는 CLAHE(Contrast-Limited Adaptive Histogram Equalization) 방법을 적용하여 영상이 어둡고 잡음이 많은 환경에서 국내 도로 이정표의 문자 정보를 얻는다. 실험 결과, 기존 방법에 비해 문자 영역 검출 성능이 향상되었다.