Automatic Roadway Geometry Measurement Algorithm Using Video Images

  • Yichang (James) Tsai
  • Jianping Wu
  • Yiching Wu
  • Zhaohua Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


The Georgia Department of Transportation (GDOT) collects and maintains an inventory of all public roads within the state. The inventory covers more than 118,000 centerline miles (188,800 km) of roads in 159 counties and over 512 municipalities. The transportation road inventory includes more than 52 items, including roadway geometry, surface type, shoulder type, speed limit signs, etc. Traditional roadway geometric properties, including number of lanes, travel lane, and shoulder widths, are measured in the field. Roadway geometric property measurement is one of the most important and, yet, the most time-consuming and riskiest component of the roadway data inventory. For the past two years, GDOT has sponsored Georgia Tech to develop a GPS/GIS-based road inventory system that re-engineers the existing paper-pencil operations. Georgia Tech has extended the research to develop video image pattern recognition algorithms and a prototype application aimed at automating the roadway geometry measurement to enhance the roadway inventory operations. A highly reliable and effective image extraction algorithm using local thresholding, predictive edge extraction, and geometric optics was developed and is presented in this paper. Preliminary results show it can effectively extract roadway features. A large-scale, experimental study on accuracy and the productivity improvement is under way.


Pavement Surface Edge Line Color Segmentation Shoulder Width Lane Marking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Tsai, Y., Wu, J.: Shape and Texture-based 1-D Image Processing Algorithm for Real-time Stop Sign Road Inventory Data Collection. Journal of Intelligent Transportation System 7, 213–234Google Scholar
  2. 2.
    Wu, J., Tsai, Y.: Speed Limit Extraction and Recognition Algorithm Using Locally Adaptive Thresholding and Depth-First-Search. Photogrammetric Engineering and Remote Sensing (PE&RS) Journal (2005) (in press)Google Scholar
  3. 3.
    Behringer, R.: Road Recognition from Multifocal Vision. In: Proceedings of IEEE Intelligent Vehicle 1994, Paris, France, pp. 302–307 (1994)Google Scholar
  4. 4.
    Kluge, K.: Extracting Road Curvature and Orientation From Image Edge Points Without Perceptual Grouping Into Features. In: Proceedings of IEEE Intelligent Vehicles 1994, Paris, France, pp. 109–114 (1994)Google Scholar
  5. 5.
    Pomerleau, D.: RALPH:Rapidly Adapting Lateral Position Handler. In: Proceedings of IEEE Intelligent Vehicle Symposium, Detroit, MI, USA, September 1995, pp. 506–511 (1995)Google Scholar
  6. 6.
    Kluge, K.: A Deformable-template approach to lane detection. In: Proceedings of IEEE Intelligent Vehicle Symposium, Detroit, MI, USA, September 1995, pp. 54–59 (1995)Google Scholar
  7. 7.
    Kreucher, C., Lakshmanan, S., Kluge, K.: A Driver Warning System based on the LOIS Lane Detection Algorithm. In: Proceedings of IEEE International Conference on Intelligent Vehicles, Stuttgart, October 28, vol. 30, pp. 17–22 (1998)Google Scholar
  8. 8.
    Kreucher, C., Lakshmanan, S.: LANA: A Lane Extraction Algorithm that Uses Frequency Domain Features. IEEE Transactions On Robotics And Automation 15, 343–350 (1999)CrossRefGoogle Scholar
  9. 9.
    Takahashi, A., Ninomiya, Y., Ohta, M., Tange, K.A.: Robust Lane Detection using Real-time Voting Processor. In: Proceedings of the IEEE Intelligent Transportation Systems, Tokyo, Japan, October 5-8, pp. 76–79 (1999)Google Scholar
  10. 10.
    Apostoloff, N., Zelinsky, A.: Robust Vision based Lane Tracking using Multiple Cues and Particle Filtering. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2003, Columbus, Ohio, USA, June 9-11, pp. 558–563 (2003)Google Scholar
  11. 11.
    Gern, A., Moebus, R., Franke, U.: Vision-based Lane Recognition under Adverse Weather Conditions Using Optical Flow. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2002, Versailles, France, June 17-21, pp. 652–657 (2002)Google Scholar
  12. 12.
    Xu, Y., Wang, R., Li, B., Ji, S.: A Vision Navigation Algorithm Based on Linear Lane Model. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2000, Dearborn (MI), USA, October 3-5, pp. 240–245 (2000)Google Scholar
  13. 13.
    Bertozzi, M., Broggi, A., Cellario, M., Fascioli, A., Lombardi, P., Porta, M.: Artificial Vision in Road Vehicles. In: Proceedings of the IEEE - Special issue on Technology and Tools for Visual Perception, vol. 90(7), pp. 1258–1271 (July 2002)Google Scholar
  14. 14.
    Bucher, T.: Measurement of Distance and Height in Images based on easy attainable Calibration Parameters. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2000, Dearborn(MI), USA, October 3-5, pp. 314–319 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yichang (James) Tsai
    • 1
  • Jianping Wu
    • 1
  • Yiching Wu
    • 1
  • Zhaohua Wang
    • 2
  1. 1.Geographic Information System CenterGeorgia Institute of TechnologyAtlantaUnited States
  2. 2.School of Civil and Environmental EngineeringGeorgia Institute of TechnologyAtlantaUnited States

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