Machine Vision and Applications

, Volume 25, Issue 3, pp 699–711 | Cite as

Low-cost sensor to detect overtaking based on optical flow

  • Pablo Guzmán
  • Javier Díaz
  • Jarno Ralli
  • Rodrigo Agís
  • Eduardo Ros
Special Issue Paper


The automotive industry invests substantial amounts of money in driver-security and driver-assistance systems. We propose an overtaking detection system based on visual motion cues that combines feature extraction, optical flow, solid-objects segmentation and geometry filtering, working with a low-cost compact architecture based on one focal plane and an on-chip embedded processor. The processing is divided into two stages: firstly analog processing on the focal plane processor dedicated to image conditioning and relevant image-structure selection, and secondly, vehicle tracking and warning-signal generation by optical flow, using a simple digital microcontroller. Our model can detect an approaching vehicle (multiple-lane overtaking scenarios) and warn the driver about the risk of changing lanes. Thanks to the use of tightly coupled analog and digital processors, the system is able to perform this complex task in real time with very constrained computing resources. The proposed method has been validated with a sequence of more than 15,000 frames (90 overtaking maneuvers) and is effective under different traffic situations, as well as weather and illumination conditions.


Machine vision Intelligent sensors Collision-avoidance systems Lane-change decision aid systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gavrila, D., Kunert M., Lages, U.: A multi-sensor approach for the protection of vulnerable traffic participants—the PROTECTOR project. In: IEEE Instrumentation and Measurement Technology Conference, vol 3, pp. 2044–2048. Budapest, Hungary (2001)Google Scholar
  2. 2.
    Díaz J., Ros E., Rotter A., Muehlenberg M.: Lane change decision aid system based on motion driven vehicle tracking. IEEE Trans. Veh. Technol. 57(5), 2736–2746 (2008)CrossRefGoogle Scholar
  3. 3.
    Mota S., Ros E., Ortigosa E.M., Pelayo F.J.: Bio-inspired motion detection for blind spot overtaking monitor. Int. J. Robot. Autom. 19(4), 190–196 (2004)Google Scholar
  4. 4.
    Song, K.T., Chen, H. Y.: Lateral driving assistance using optical flow and scene analysis. In: Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, pp. 624–9 (2007)Google Scholar
  5. 5.
    Sakurai, K., Kyo, S., Okazaki S.: Implementation of overtaking vehicle detection using the IMAPCAR highly parallel image processor. In: Proc. of ITS Congress (2006)Google Scholar
  6. 6.
    Alessandretti G., Broggi A., Cerri P.: Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans. Intell. Transp. Syst. 8(1), 95–105 (2007)CrossRefGoogle Scholar
  7. 7.
    Mobileye, N.V.: Blind spot detection and lane change assist (BSD/LCA). Accessed 23 Jan 2009
  8. 8.
    Volvo BLIS System Accessed 23 Jan 2009
  9. 9.
  10. 10.
    Liu, W., Wen, X., Duan, B., Yuan, H., Wang, N.: Rear vehicle detection and tracking for lane change assist. In: Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, pp. 252–257 (2007)Google Scholar
  11. 11.
    Bertozzi, M., Broggi, A.: Real-time lane and obstacle detection on the gold system. In: IEEE Intelligent Vehicle Symposium, pp. 213–218 (1996)Google Scholar
  12. 12.
    Blanc, N., Steux, B., Hinz, T.: LaRASideCam - a fast and robust vision-based blindspot detection system. In: Proc. IEEE Intelligent Vehicles Symp., pp. 480–485 Istanbul, Turkey, 13–15 Jun (2007)Google Scholar
  13. 13.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. Seventh International Joint Conference on Artificial Intelligence, pp. 674–679. Vancouver, Canada (1981)Google Scholar
  14. 14.
    Welch, G., Bishop, G.: An introduction to the Kalman filter. Dept. Comput. Sci., Univ. North Carolina Chapel Hill, Chapel Hill, NC, Tech. Rep. TR 95-041 (2002)Google Scholar
  15. 15.
    Hassenstein, B., Reichardt, W.: Systemtheoretische Analyse der Zeit-Reihenfolgen und Vorzeichenauswertung bei der ewegungsperzeption des Rüsselkäfers Chlorophanus, Zeitschrift für Naturforschung 11b 513–524 (1956)Google Scholar
  16. 16.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of 4th Alvery Vision Conference on Manchester, pp. 189–192 (1988)Google Scholar
  17. 17.
    Song, K.T., Huang, J. H.: Fast optical flow estimation and its application to real-time obstacle avoidance. In: Proc. Of 2001 IEEE ICRA, pp. 2891–2896. Seoul, Korea (2001)Google Scholar
  18. 18.
    Broggi, A., Conte, G., Gregoretti, F., Sansoè, C., Reyneri, L. M.: The evolution of the PAPRICA system. Integr. Comput. Aided Eng. J. 4(1) (1996) (special issue on massively parallel computing)Google Scholar
  19. 19.
    Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)Google Scholar
  20. 20.
    Rodríguez-Vázquez, Á., Domínguez-Castro, R., Jiménez-Garrido, F.: The Eye-RIS CMOS vision system, analog circuit design: sensors, actuators and power drivers, pp. 15–32. Springer, Berlin (2008)Google Scholar
  21. 21.
    Dudek P., Carey S.J.: A general-purpose 128 × 128 SIMD processor array with integrated image sensor. Electron. Lett. 42(12), 678–679 (2006)CrossRefGoogle Scholar
  22. 22.
    Dudek, P., W.Barr, D.R., Lopich, A., Carey, S.J.: Demonstration of real-time image processing on the SCAMP-3 vision system. In: IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006, pp. 13–13. Istanbul, August 2006Google Scholar
  23. 23.
    Foldesy P., Zarandy A., Rekeczky C.: Configurable 3D-integrated focal-plane cellular sensor-processor array architecture. Int. J. Circuit Theory Appl. 36(5–6), 573–588 (2008)CrossRefGoogle Scholar
  24. 24.
    Zarándy, Á., Rekeczky, C., Földesy, P.: Analysis of 2D operators on topographic and non-topographic processor architectures. CNNA 2008. In: 11th international workshop on cellular neural networks and their applications. Santiago de Compostela, IEEE (2008)Google Scholar
  25. 25.
    Guzmán, P., Díaz, J., Agís, R., Ros, E.: Optical flow in a smart sensor based on hybrid analog-digital architecture. Sensors 10, 2975–2994 (2010) (special issue on motion sensors)Google Scholar
  26. 26.
    Davis L.S.: A survey of edge detection techniques. Comput. Graph. Image Process. 4(3), 248–270 (1975)CrossRefGoogle Scholar
  27. 27.
    Liu H., Hong T., Herman M., Camus T., Chellapa R.: Accuracy vs. efficiency trade-off in optical flow algorithms. Comput. Vis. Image Underst. 72, 271–286 (1998)CrossRefGoogle Scholar
  28. 28.
    Gat I., Benady M., Shashua A.: A monocular vision advance warning system for the automotive aftermarket. SAE Trans. 114(7), 403–410 (2005)Google Scholar
  29. 29.
    de la Escalera, A.: Visión por computador: fundamentos y métodos. Prentice Hall, Englewood Cliffs (2001)Google Scholar
  30. 30.
    Finlay D.J., Dodwell P.C., Caelli T.M.: The wagon-wheel effect. Perception 13, 237–248 (1984)CrossRefGoogle Scholar
  31. 31.
    Green M.: How long does it take to stop? Methodological analysis of driver perception-brake times. Transp. Hum. Factors 2(3), 195–216 (2000)CrossRefGoogle Scholar
  32. 32.
    I-Car: blind spot object detection systems. Accessed 23 Jan 2009
  33. 33.
    Volvo Club: Volvo S60. Press Information. Accessed 23 Jan 2009
  34. 34.
    O’Malley, R., Glavin, M., Jones, E.: Vehicle detection at night based on tail-light detection. In: 1st International Symposium on Vehicular Computing Systems, Trinity College, Dublin, July 2008Google Scholar
  35. 35.
    Alcantarilla, P.F., Bergasa, L.M., Jimenez, P., Sotelo, M.A., Parra, I., Fernandez, D.: Night time vehicle detection for driving assistance lightBeam controller. In: IEEE Intelligent Vehicles Symposium. Eindhoven, The Netherlands, 4–6 June 2008Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Pablo Guzmán
    • 1
  • Javier Díaz
    • 1
  • Jarno Ralli
    • 1
  • Rodrigo Agís
    • 1
  • Eduardo Ros
    • 1
  1. 1.Department of Computer Architecture and Technology, ETSI Informática y de Telecomunicación, CITIC-UGRUniversity of GranadaGranadaSpain

Personalised recommendations