Detection of Traffic Lights for Vision-Based Car Navigation System

  • Hwang Tae-Hyun
  • Joo In-Hak
  • Cho Seong-Ik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


A recent trend of car navigation system is using actual video captured by camera equipped on a vehicle. The video-based navigation systems displays guidance information overlaid onto video before reaching a crossroad, so it is essential to detect where the crossroads are in the video frame. In this paper, we suggest a detection method for traffic lights that is used for estimating location of crossroads in image. Suggested method can detect traffic lights in a long distance, and estimates pixel location of crossroad that is important information to visually represent guidance information on video. We suggest a new method for traffic light detection that processes color thresholding, finds center of traffic light by Gaussian mask, and verifies the candidate of traffic light using suggested existence-weight map. Experiments show that the detection method for traffic signs works effectively and robustly for outdoor video and can used for video-based navigation system.


Navigation System Candidate Region Traffic Light Light Detection Actual Video 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Narzt, W., Pomberger, G., Ferscha, A., Kolb, D., Muller, R., Wieghardt, J., Hortner, H., Lindinger, C.: Pervasive Information Acquisition for Mobile AR-Navigation Systems. In: 5th IEEE Workshop on Mobile Computing Systems & Applications, Monterey, California, USA, October 2003, pp. 13–20 (2003)Google Scholar
  2. 2.
    Hu, Z., Uchimura, K.: Solution of Camera Registration Problem Via 3D-2D Parameterized Model Matching for On-Road Navigation. International Journal of Image and Graphics 4(1), 3–20 (2004)CrossRefGoogle Scholar
  3. 3.
    de la Escalera, A., Armingol, J.M., Pastor, J.M., Rodriguez, F.J.: Visual Sign Information Extraction and Identification by Deformable Models for Intelligent Vehicles. IEEE Transactions on Intelligent Transportation Systems 5(2) (June 2004)Google Scholar
  4. 4.
    Tu, Z., Li, R.: Automatic Recognition of Civil Infrastructure Objects in Mobile Mapping Imagery Using Markov Random Field. In: Proc. of ISPRS Conf. 2000, Amsterdam (July 2000)Google Scholar
  5. 5.
    Hwang, T.-H., Cho, S.-I., Park, J.-H., Choi, K.-H.: Object Tracking for a Video Sequence from a Moving Vehicle: A Multi-modal Approach. ETRI Journal 28(3), 367–370 (2006)CrossRefGoogle Scholar
  6. 6.
    National Police Agency of Korea, The Standard Guideline for Colors of Traffic Signs (2004)Google Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.C.: Digital Image Processing. Addison-Wesley, Reading (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hwang Tae-Hyun
    • 1
  • Joo In-Hak
    • 1
  • Cho Seong-Ik
    • 1
  1. 1.Telematics.USN Research DivisionETRIDaejeonRepublic of Korea

Personalised recommendations