Abstract
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.
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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, 213–234
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)
Behringer, R.: Road Recognition from Multifocal Vision. In: Proceedings of IEEE Intelligent Vehicle 1994, Paris, France, pp. 302–307 (1994)
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)
Pomerleau, D.: RALPH:Rapidly Adapting Lateral Position Handler. In: Proceedings of IEEE Intelligent Vehicle Symposium, Detroit, MI, USA, September 1995, pp. 506–511 (1995)
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)
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)
Kreucher, C., Lakshmanan, S.: LANA: A Lane Extraction Algorithm that Uses Frequency Domain Features. IEEE Transactions On Robotics And Automation 15, 343–350 (1999)
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)
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)
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)
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)
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)
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)
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Tsai, Y.(., Wu, J., Wu, Y., Wang, Z. (2005). Automatic Roadway Geometry Measurement Algorithm Using Video Images. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_82
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DOI: https://doi.org/10.1007/11553595_82
Publisher Name: Springer, Berlin, Heidelberg
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