Advertisement

Journal of Real-Time Image Processing

, Volume 11, Issue 1, pp 179–191 | Cite as

Real-time lane tracking using Rao-Blackwellized particle filter

  • Marcos NietoEmail author
  • Andoni Cortés
  • Oihana Otaegui
  • Jon Arróspide
  • Luis Salgado
Original Research Paper

Abstract

A novel approach to real-time lane modeling using a single camera is proposed. The proposed method is based on an efficient design and implementation of a particle filter which applies the concepts of the Rao-Blackwellized particle filter (RBPF) by separating the state into linear and non-linear parts. As a result the dimensionality of the problem is reduced, which allows the system to perform in real-time in embedded systems. The method is used to determine the position of the vehicle inside its own lane and the curvature of the road ahead to enhance the performance of advanced driver assistance systems. The effectiveness of the method has been demonstrated implementing a prototype and testing its performance empirically on road sequences with different illumination conditions (day and nightime), pavement types, traffic density, etc. Results show that our proposal is capable of accurately determining if the vehicle is approaching the lane markings (Lane Departure Warning), and the curvature of the road ahead, achieving processing times below 2 ms per frame for laptop CPUs, and 12 ms for embedded CPUs.

Keywords

Kalman Filter Particle Filter Lane Change Obstacle Detection Embed Processor 
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.

Notes

Acknowledgments

This work has been partially supported by the program ETORGAI 2011-2013 of the Basque Government under project IEB11. This work has been possible thanks to the cooperation with Datik–Irizar Group for their support in the installation, integration and testing stages of the project.

References

  1. 1.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/nonGaussian Bayesian tracking. IEEE. Trans. Signal. Process. 50(2), 174–188 (2002)CrossRefGoogle Scholar
  2. 2.
    Borkar, A., Hayes, M., Smith, M.T.: Lane detection and tracking using a layered approach. In: Proceedings of Advanced Concepts for Intelligent Vision Systems, pp. 474–484. (2009)Google Scholar
  3. 3.
    Borkar, A., Hayes, M., Smith, M.T.: A new multi-camera approach for lane departure warning. In: Proceedings of Advanced Concepts for Intelligent Vision Systems, pp. 59–69. (2011)Google Scholar
  4. 4.
    Chiu, C.-C., Chung, M.-L., Chen, W.-C.: Real-time front vehicle detection algorithm for an asynchronous binocular system. J. Inf. Sci. Eng. 26(3), 735–752 (2010)Google Scholar
  5. 5.
    Corridori, C., Zanin, M.: High curvature two-clothoid road model estimation. In: IEEE Proceedings of Intelligent Transportation Systems Conference, pp. 630–636. (2004)Google Scholar
  6. 6.
    Danescu, R., Nedevschi, S., Meinecke, M.-M., To, T.-B.: A stereovision-based probabilistic lane tracker for difficult road scenarios. In: IEEE Proceedings of Intelligent Vehicles Symposium, pp. 536–541. (2008)Google Scholar
  7. 7.
    del Blanco, C.R., Jaureguizar, F., García, N.: An advanced Bayesian model for the visual tracking of multiple interacting objects. EURASIP J. Adv. Signal Process. 130 (2011)Google Scholar
  8. 8.
    Dickmanns, E.D., Graefe, V.: Dynamic monocular machine vision. MachineVision Appl. 1(4), 223–240 (1988)Google Scholar
  9. 9.
    Doucet, A., Gordon, N.J., Krishnamurthy, V.: Particle filters for state estimation of jump Markov linear systems. IEEE Trans. Signal Process. 49(3), 613–624 (2001)CrossRefGoogle Scholar
  10. 10.
    Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1819 (2005)CrossRefGoogle Scholar
  11. 11.
    Kim, Z.: Robust lane detection and tracking in challenging scenarios. IEEE. Trans. Intell. Transp. Syst. 9(1), 16–26 (2008)CrossRefGoogle Scholar
  12. 12.
    McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system and evaluation. IEEE. Trans. Intell. Transp. Syst. 7(1), 20–37 (2006)CrossRefGoogle Scholar
  13. 13.
    Nieto, M., Arróspide, J., Salgado, L.: Road environment modeling using robust perspective analysis and recursive Bayesian segmentation. Mach. Vis. Appl. 22(6), 927–945 (2011)CrossRefGoogle Scholar
  14. 14.
    Smal, I., Niessen, W., Meijering, E.: Advanced particle filtering for multiple object tracking in dynamic fluorescence microscopy images. In: IEEE Proceedings of International Symposium on Biomedical Imaging, pp. 1048–1051. (2007)Google Scholar
  15. 15.
    Southall, B., Taylor, C.J.: Stochastic road shape estimation. In: Proceedings of International Conference on Computer Vision, vol. 1, pp. 205–212. (2001)Google Scholar
  16. 16.
    Wang, Y., Teoh, E.K., Shen, D.: Lane detection and tracking using B-snakes. Image. Vis. Comput. 22, 269–289 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcos Nieto
    • 1
    Email author
  • Andoni Cortés
    • 1
  • Oihana Otaegui
    • 1
  • Jon Arróspide
    • 2
  • Luis Salgado
    • 2
  1. 1.Vicomtech-ik4Donostia–San SebastiánSpain
  2. 2.Grupo de Tratamiento de ImágenesUniversidad Politécnica de MadridMadridSpain

Personalised recommendations