Advertisement

Computationally Efficient Vehicle Tracking for Detecting Accidents in Tunnels

  • Gyuyeong Kim
  • Hyuntae Kim
  • Jangsik Park
  • Jaeho Kim
  • Yunsik Yu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 263)

Abstract

It is becoming increasingly important to construct tunnel for transportation time and space utilization. To avoid the large scale of damages of vehicle accident in the tunnel, it is necessary to have a tunnel accidents monitoring system to minimize and discover the accidents as fast as possible. In this paper, a moving and stopped vehicle detection algorithm is proposed. It Detecting vehicle based on morphological size information of object according to distance and Adaboost algorithm. Kalman filter and LUV color informations of rear lamp are used to detect stopped vehicles. Results of computer simulations show that proposed algorithm increases detection rate more than other detection algorithms.

Keywords

Background estimation Adaboost Algorithm Kalman Filter LUV color 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, G., Kim, H., Park, J., Yu, Y.: Vehicle Tracking Based on Kalman Filter in Tunnel. In: Kim, T.-h., Adeli, H., Robles, R.J., Balitanas, M. (eds.) ISA 2011. CCIS, vol. 200, pp. 250–256. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Freund, Y., Schapire, R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (2001)Google Scholar
  4. 4.
    Barron, J.L., et al.: Systems and Experiment In: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Watman, C., Austin, D.: Fast sum of absolute differences visual landmark detector. In: Proceedings IEEE Conf. on Robotics and Automation (2004)Google Scholar
  6. 6.
    Welch, G., Bishop, G.: An introduction to the Kalman filter. UNC-Chapel Hill, TR 95-041, July 24 (2006)Google Scholar
  7. 7.
    Rad, R., Jamzad, M.: Real time classification and tracking of multiple vehicles in highways, vol. 26, pp. 1597–1607. Elsevier (2005)Google Scholar
  8. 8.
    Massimo, P.: Background subtraction technique: a review. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3099–3104 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gyuyeong Kim
    • 1
  • Hyuntae Kim
    • 2
  • Jangsik Park
    • 3
  • Jaeho Kim
    • 4
  • Yunsik Yu
    • 1
  1. 1.Convergence of IT Devices Institute BusanBusanKorea
  2. 2.Department of Multimedia EngineeringDongeui UniversityBusanKorea
  3. 3.Department of Electronics EngineeringKyungsung UniversityBusanKorea
  4. 4.Department of Electronics EngineeringPusan National UniversityBusanKorea

Personalised recommendations