영상인식과 사진측량 기술을 이용한 교통표지 자동측정 방법

다국어 초록 (Multilingual Abstract)

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.

참고문헌 (Reference)

1 최미순, "영상분할 및 Haar-like 특징 기반 자동차 검출" 한국멀티미디어학회 13 (13): 1314-1321, 2010

2 윤창용, "수직면과 아다부스트를 사용한 실시간 교통 표지판 검출" 대한전자공학회 45 (45): 29-37, 2009

3 Shin, M. C., "Traffic sign recognition using color information and neural networks" Inha University 2006

4 Auranuch Lorsakul, "Traffic sign recognition for intelligent vehicle/driver assistance system using neural network on OpenCV" 2007

5 Yoo, B., "The Development of real-time photogrammetric system using digital photo- grammetry and GPS" Korea Institute of Construction Technology 2000

6 C. Kotropoulos, "Rule-based face detection in frontal views" 4 : 2537-3541, 1997

7 Viola, P., "Rapid object detection using a Boosted cascade of simple features" 1 : 511-518, 2001

8 박장한, "PCA기반의 스테레오 얼굴영상에서 거리에 따른 인식률 비교" 대한전자공학회 42 (42): 9-16, 2005

9 R. Lienhart, "Localizing and segmenting text in images and videos" 12 (12): 256-269, 2002

10 Asakura, T., "Image recognition of traffic speed marks using neural networks" 61 (61): 206-212, 1994

11 Samaria, F., "Face recognition using hidden markov models" University of Cambridge 1994

12 Min, J. H., "Face detection ＆ amp; Identification system using Haar-like feature/HMM" Korean Institute of Information Scientists and Engineers 31 (31): 739-741, 2004

13 Kim, D. H., "Face detection and tracking using skin color information and Haar-like features in real-time video" Korean HCI Society 146-149, 2009

14 Paul R. Wolf, "Elements of photogrammetry : with application in GIS" McGraw- Hill Inc 2000

15 Jeong, D. H., "Development of car MMS S/W for road facilities DB construction" Korea Spatial Information Society 89-93, 2002

16 Jeong, M. H., "Blunder detection by matching strength measurement in digital photogrammetry" Sungkyunkwan University 2000

17 박성효, "Adaboost를 이용한 모바일 환경에서의 홍채인식을 위한눈 검출에 관한 연구" 대한전자공학회 45 (45): 1-11, 2008

18 Di Zenzo, S., "A note on the gradient of a multi image" 33 : 116-125, 1986