IMAGE PROCESSING TECHNIQUES FOR LANE-RELATED INFORMATION EXTRACTION AND MULTI-VEHICLE DETECTION IN INTELLIGENT HIGHWAY VEHICLES

  • Wu, Y.J. (Department of Civil Engineering, National Taiwan University) ;
  • Lian, F.L. (Department of Electrical Engineering, National Taiwan University) ;
  • Huang, C.P. (Department of Electrical Engineering, National Taiwan University) ;
  • Chang, T.H. (Department of Civil Engineering, National Taiwan University)
  • Published : 2007.08.31

Abstract

In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

Keywords

References

  1. Bertozzi, M. and Broggi, A. (1998). GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Processing 7, 1, 62-81 https://doi.org/10.1109/83.650851
  2. Bertozzi, M. Broggi, A. Fascioli, A. and Tibaldi, A. (2002). An evolutionary approach to lane markings detection in road environments. Atti del 6 Convegno dell'Associazione Italiana per l'Intelligenza Artificiale. 627-636, Siena, Italy
  3. Betke, M., Haritaoglu, E. and Davis, L. S. (2000). Realtime multiple vehicle detection and tracking from a moving vehicle. Int. J. Machine Vision and Applications 12, 2, 69-83 https://doi.org/10.1007/s001380050126
  4. Blackman, S. S. (1986). Multiple-Target Tracking with Radar Applications. Artech House. Norwood, MA
  5. Chang, T.-H., Lin, C.-H., Hsu, C.-S. and Wu, Y.-J. (2003). A vision-based vehicle behavior monitoring and warning system. Proc. IEEE 6th Int. Conf. Intelligent Transportation Systems, Shanghai, China, 448-453
  6. Fang, C.-Y. (2003). A Vision-Based Driver Assistance System Based on Dynamic Visual Model. Ph. D. Dissertation. Department of Computer Science and Information Engineering. National Taiwan University
  7. Jain, R., Kasturi, R. and Schunck, B. G. (1995). Machine Vision McGRAW-Hill Int. Edn
  8. Jeong, S. G., Kim, C. S., Lee, D. Y., Ha, S. K., Lee, D. H., Lee, M. H. and Hashimoto, H. (2001). Real-time lane detection for autonomous vehicle. Proc. IEEE Int. Symp. Industrial Electronics, 3, Pusan, Korea, 1466-1471
  9. Van Leeuwen, M. B. and Groen, F. C. A. (2005). Vehicle detection with a mobile camera: Spotting midrange, distant and passing cars. IEEE Robotics & Automation Magazine 12, 1, 37-43 https://doi.org/10.1109/MRA.2005.1411417
  10. Park, J. W., Lee, J. W. and Jhang, K. Y. (2003). A lanecurve detection based on LCF. Pattern Recognition Letters, 24, 2301-2313 https://doi.org/10.1016/S0167-8655(03)00056-4
  11. Shapiro, L. G. and Stockman, G. C. (2001). Computer Vision. Prentice Hall. New Jersey
  12. Srinivasa, N. (2002). Vision-based vehicle detection and tracking method for forward collision warning in automobiles. Proc. IEEE Intelligent Vehicle Symp. 2, 626-631
  13. Tzomakas, C. and von Seelen, W. (1998). Vehicle Detection in Traffic Scenes Using Shadows. Internal Report IR-INI 98-06. Institut fur Neuroinformatik. Ruhr-Universitat. Bochum. Germany
  14. Yen, P.-S. (2003). Motion Analysis of Nearby Vehicles on a Freeway. Master Thesis. Department of Information & Computer Education. National Taiwan Normal University
  15. Yi, U.-K. Lee, J. W. and Baek, K. R. (2005). A fuzzy neural network-based decision of road image quality for the extraction of lane-related information. Int. J. Automotive Technology 6, 1, 53-63
  16. Yi, U.-K. and Lee, J. W. (2005). Extraction of lanerelated information and a real-time image processing onboard system. Int. J. Automotive Technology 6, 2, 171-181