Parallel Hough Space Image Generation Method for Real-Time Lane Detection

  • Hee-Soo KimEmail author
  • Seung-Hae Beak
  • Soon-Yong Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)


This paper proposes a new parallelization method to generate Hough space images for real-time lane detection, using the new NVIDIA Jetson TK1 board. The computation cost in Standard Hough Transform is relatively high due to its higher amount of unnecessary operations. Therefore, in this paper, we introduce an enhanced Hough image generation method to reduce computation time for real-time lane detection purposes, and reduce all the unnecessary operations exist in the Standard method. We implemented our proposed method in both CPU and GPU based platforms and compared the processing speeds with the Standard method. The experiment results induce that the proposed method runs 10 times faster than the existing method in CPU platform, whereas 60 times faster in the GPU platform.


Hough space image Lane detection Lane Departure Warning System(LDWS) CUDA Advance Driver Assistant System(ADAS) 



This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) (IITP-2016-H8601-16-1002) supervised by the IITP(Institute for Information & communications Technology Promotion).


  1. 1.
    Kim, S., Kim, J.H.: Adaptive fuzzy-network-based C-measure map-matching algorithm for car navigation system. IEEE Trans. Indus. Electron. 48(2), 432–441 (2011)Google Scholar
  2. 2.
    Suzuki, K., Jansson, H.: An analysis of driver’s steering behaviour during auditory or haptic warnings for the designing of lane departure warning system. JSAE Rev. 24(1), 65–70 (2003)CrossRefGoogle Scholar
  3. 3.
    Jung, H.G., Kim, D.S., Yoon, P.J., Kim, J.: Parking slot markings recognition for automatic parking assist system. In: IEEE Intelligent Vehicles Symposium, pp. 106–113 (2006)Google Scholar
  4. 4.
    Sparbert, J., Dietmayer, K., Streller, D.: Lane detection and street type classification using laser range images. IEEE Intelligent Transportation System Conference, pp. 454–459 (2001)Google Scholar
  5. 5.
    Lindner, P., Richter, E., Wanielik, G., Takagi, K., Isogai, A.: Multi-channel lidar processing for lane detection and estimation. IEEE Conference on Intelligent Transportation, pp. 1–6 (2009)Google Scholar
  6. 6.
    Wang, Y., Teoh, E.K., Shen, D.: Lane detection and tracking using B-Snake. Image Vis. Comput. 22(4), 269–280 (2004)CrossRefGoogle Scholar
  7. 7.
    Lee, S.G., Choi, G.H., Bae J.G., Lee, H.J., Choi, S.Y., Hyun, S.H., Han, D.S.: A study on awareness method of lane color using HSV color model for lane departure warning system. In: Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp. 291–293 (2014)Google Scholar
  8. 8.
    He, Y., Wang, H., Zhang, B.: Color-based road detection in urban traffic scenes. IEEE Trans. Intell. Transp. Syst. 5(4), 309–318 (2004)CrossRefGoogle Scholar
  9. 9.
    Lin, Q., Han, Y., Hahn, H.: Real-time lane departure detection based on extended edge-linking algorithm. In: International Conference on Computer Research and Development, pp. 725–730 (2010)Google Scholar
  10. 10.
    Hardzeyeu, V., Klefenz, F.: On using the hough transform for driving assistance applications. In: International Conference on Intelligent Computer Communication and Processing, pp. 91–98 (2008)Google Scholar
  11. 11.
    Jang, H.J., Baek, S.H., Park, S.Y.: Lane marking detection in various lighting conditions using robust feature extraction. In: International Conference in Central Europe on Computer Graphics, pp. 83–88 (2014)Google Scholar
  12. 12.
    Wang, J., Wu, Y., Liang, Z., Xi, Y.: Lane detection based on random hough transform on region of interesting. In: IEEE International Conference in Information and Automation, pp. 1735–1740 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringKyungpook National UniversityDaeguRepublic of Korea

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