Luminance Adaptive Dynamic Background Models for Vision-Based Traffic Detection

  • Nazmul Haque
  • Md. HadiuzzamanEmail author
  • Md. Yusuf Ali
  • Farhana Mozumder Lima
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 879)


Measuring traffic flow by employing vision-based detection suffers from several challenges, particularly the illumination variation. Consequently, this research focuses on solving traffic detection problem due to both sudden and gradual illumination changes. A number of theories are proposed to define different components of an image. Specifically, first-order model for illumination variation and Fourier series for incorporating traffic arrival patterns are considered to define background and foreground, respectively. We have utilized these definitions to formulate the traffic detection problem and subsequently three adaptive dynamic background models have been developed to solve it. The third model that incorporates both luminance and pollution controlling parameters fixes the problems and limitations faced by the first and second models. Besides, a new per pixel binary threshold model related to the third model is also developed for foreground segmentation. Using a real video dataset, a constrained optimization is performed to determine the optimal values of model parameters, where the feasible regions of the parameters are obtained graphically. The model validation using a separate video dataset shows more than 95% Percent Correct Classification (PCC) value and around 90% Precision and Recall values. Additionally, a field test is conducted in three different locations and the performance of the model is evaluated. Evaluation shows that, the model achieves the highest value of 93% in terms of Average Accuracy of Object Count (AAOC) for urban arterial dataset, which represents its robustness in object detection.


Illumination variation Traffic detection Background modeling Graphical optimization 


  1. 1.
    Lee, B., Hedley, M.: Background estimation for video surveillance. Image Vis. Comput. 1, 315–320 (2002)Google Scholar
  2. 2.
    McFarlane, N.J.B., Schofield, C.P.: Segmentation and tracking of piglets in images. Br. Mach. Vis. Appl. 1, 187–193 (1995)CrossRefGoogle Scholar
  3. 3.
    Zheng, J., Wang, Y., Nihan, N., Hallenbeck, M.: Extracting roadway background image: mode-based approach. Transp. Res. Rec. J. Transp. Res. Board 1944, 82–88 (2006)Google Scholar
  4. 4.
    Wren, C.R., Porikli, F.: Waviz: Spectral similarity for object detection. In: Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance 2005, pp. 55–61 (2005)Google Scholar
  5. 5.
    Kim, H., Sakamoto, R., Kitahara, I., Toriyama, T., Kogure, K.: Robust foreground extraction technique using Gaussian family model and multiple thresholds. In: Proceedings of the Asian Conference on Computer Vision 2007, pp. 758–768 (2007)Google Scholar
  6. 6.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1999 (1999)Google Scholar
  7. 7.
    Guo, L., Du, M.H.: Student’s t-distribution mixture background model for efficient object detection. In: Proceedings of the IEEE International Conference on Signal Processing, Communication and Computing 2012, pp. 410–414 (2012)Google Scholar
  8. 8.
    Haines, T.S., Xiang, T.: Background subtraction with dirichlet processes. In: Proceedings of the European Conference on Computer Vision 2012, pp. 99–113 (2012)Google Scholar
  9. 9.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Proceedings of the European Conference on Computer Vision 2000, pp. 751–767 (2000)Google Scholar
  10. 10.
    Ding, X., He, L., Carin, L.: Bayesian robust principal component analysis. IEEE Trans. Image Process. 20(12), 3419–3430 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Barnich, O., Droogenbroeck, M.V.: ViBe: a powerful random technique to estimate the background in video sequences. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2009, pp. 945–948 (2009)Google Scholar
  12. 12.
    Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2012, pp. 38–43 (2012)Google Scholar
  13. 13.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B: Wallflower: principles and practice of background maintenance. In: Proceedings of the 7th IEEE International Conference on Computer Vision 1999, vol. 1, pp. 255–261 (1999)Google Scholar
  14. 14.
    Karnna, K.P., Raja, Y., Gong, S.: Moving object recognition using an adaptive background memory. Time-Varying Image Process. Mov. Object Recognit. 2, 289–307 (1990)Google Scholar
  15. 15.
    Tezuka, H., Nishitani, T.: A precise and stable foreground segmentation using fine-to-coarse approach in transform domain. In: Proceedings of the 15th IEEE International Conference on Image Processing 2008, pp. 2732–2735 (2008)Google Scholar
  16. 16.
    Gao, T., Liu, Z.G., Gao, W.C., Zhang, J.: A robust technique for background subtraction in traffic video. In: Proceedings of the International Conference on Neural Information Processing 2008, pp. 736–744 (2008)Google Scholar
  17. 17.
    Guan, Y.P.: Wavelet multi-scale transform based foreground segmentation and shadow elimination. Open Signal Process. J. 1(6), 1–6 (2008)CrossRefGoogle Scholar
  18. 18.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)Google Scholar
  19. 19.
    Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: Change Detection Challenge. Accessed 21 June 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nazmul Haque
    • 1
  • Md. Hadiuzzaman
    • 1
  • Md. Yusuf Ali
    • 1
  • Farhana Mozumder Lima
    • 1
  1. 1.Department of Civil EngineeringBangladesh University of Engineering and Technology (BUET)DhakaBangladesh

Personalised recommendations