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Night Time and Low Visibility Driving Assistance Based on the Application of Colour and Geometrical Features Extraction

  • Henry CruzEmail author
  • Juan Meneses
  • Martina Eckert
  • José F. Martínez
Conference paper
  • 2.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9704)

Abstract

The present work shows an application to detect cars in night environments as a means to assist the car driving through HSV (Hue, Saturation, Value) colour extraction and geometric modelling. The developed algorithm has been implemented in smart devices through the platform Android. The detection and tracking of vehicles are implemented in low visibility environments such as night time, raining or snowing conditions; the different tests carried out confirm high performance rates of detection (p = 95.2 %). The information provided by the different sensors of the smart devices have been used to generate virtual information in a real driving environment (Augmented Reality) in order to complement the functionalities of the purposed solution. This information consists of visual, vibratory and auditory warnings that detect possible collisions and dangerous driving situations.

Keywords

Color extraction Geometrical features Smart devices Augmented reality Assisted driving 

Notes

Acknowledgment

This work was supported by Spanish National Plan for Scientific Technical Research and Innovation, project number TEC2013-48453-C2-2-R.

References

  1. 1.
  2. 2.
    Russ, A., Wagner, A.S., Liesner, L., Küçükay, F., Vink, P.: Flow experience influenced by car adjustments. Transp. Res. Traffic Psychol. Behav. 36, 46–56 (2016)CrossRefGoogle Scholar
  3. 3.
    Lee, J.D., McGehee, D.V., Brown, T.L., Reyes, M.L.: Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Hum. Factors: J. Hum. Factors Ergon. Soc. 44(2), 314–334 (2002)CrossRefGoogle Scholar
  4. 4.
    Dhanasekaran, S., Ramachandran, K., Selvamuthukumar, M., Velam, N., Pal, S.: A survey on vehicle detection based on vision. Mod. Appl. Sci. 9(12), 118 (2015)CrossRefGoogle Scholar
  5. 5.
    Digregorio, B.: Safer driving in the dead of night. Spectr. IEEE 43(3), 20–21 (2006)CrossRefGoogle Scholar
  6. 6.
    Schenkman, B.N., Brunnström, K.: Camera position and presentation scale for infrared night vision systems in cars. Hum. Factors Man. 17, 457–473 (2007)CrossRefGoogle Scholar
  7. 7.
    Gritsch, G., Donath, N., Kohn, B., Litzenberger, M.: Night-time vehicle classification with an embedded, vision system. In: Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, pp. 1–6 (2009)Google Scholar
  8. 8.
    Chen, Y.L., Chen, Y.H., Chen, C.J., Wu, B.F.: Nighttime vehicle detection for driver assistance and autonomous vehicles. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 1, pp. 687–690 (2006)Google Scholar
  9. 9.
    Stein, G.P., Mano, O., Shashua, A.: Vision-based ACC with a single camera: bounds on range and range rate accuracy. Proc. IEEE Intell. Veh. Symp. 2003, 120–125 (2003)CrossRefGoogle Scholar
  10. 10.
    Cabani, I., Toulminet, G., Bensrhair, A.: Color-based detection of vehicle lights. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 278–283. IEEE, June 2005Google Scholar
  11. 11.
    Chan, Y.M., Huang, S.S., Fu, L.C., Hsiao, P.Y.: Vehicle detection under various lighting conditions by incorporating particle filter. In: IEEE Intelligent Transportation Systems Conference ITSC 2007, pp. 534–539. IEEE, September 2007Google Scholar
  12. 12.
    Chen, Y.L., Wu, B.F., Lin, C.T., Fan, C.J., Hsieh, C.M.: Real-time vision-based vehicle detection and tracking on a moving vehicle for nighttime driver assistance. Int. J. Robot. Autom. 24(2), 89–102 (2009)Google Scholar
  13. 13.
    Chen, Y.L., Chiang, H.H., Chiang, C.Y., Liu, C.M., Yuan, S.M., Wang, J.H.: A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture. Sensors 12(3), 2373–2399 (2012)CrossRefGoogle Scholar
  14. 14.
    Wang, J., Sun, X., Guo, J.: A region tracking-based vehicle detection algorithm in nighttime traffic scenes. Sensors 12, 16474–16493 (2013)CrossRefGoogle Scholar
  15. 15.
    Ohn-Bar, E., Tawari, A., Martin, S., Trivedi, M.M.: On surveillance for safety critical events: in-vehicle video networks for predictive driver assistance systems. Comput. Vis. Image Underst. 134, 130–140 (2015)CrossRefGoogle Scholar
  16. 16.
    Kaplan, S., Guvensan, M.A., Yavuz, A.G., Karalurt, Y.: Driver behavior analysis for safe driving: a survey. IEEE Trans. Intell. Transp. Syst. 16, 3017–3032 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Henry Cruz
    • 1
    Email author
  • Juan Meneses
    • 1
  • Martina Eckert
    • 1
  • José F. Martínez
    • 1
  1. 1.Research Center on Software Technologies and Multimedia Systems for SustainabilityTechnical University of MadridMadridSpain

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