Skip to main content
Springer Nature Link
Log in

Real Time Fuzzy Based Traffic Flow Estimation and Analysis

  • Conference paper
  • First Online:
New Knowledge in Information Systems and Technologies (WorldCIST'19 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 931))

Included in the following conference series:

  • 2116 Accesses

Abstract

Real-time traffic flow analysis using road mounted surveillance cameras presents multitude of benefits. In this paper, we used surveillance videos to design optical flow based technique for robust motion analysis and estimation. Region growing method is employed for detection of objects of interest. Autonomous density estimation of vehicles is crucial for traffic congestion analysis so that countermeasures can be taken at the earliest possible opportunity. A video based data extraction scheme for traffic data is proposed to determine the right traffic conditions which alleviates the false alarms and detrimental noise effects. Evaluation of proposed system is done by applying approach on several surveillance videos obtained from different sources and scenarios. An experimental study illustrates estimation and analysis results accuracy as compared to state-of-the-art approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ozkurt, C.: Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Math. Comput. Appl. 14(3), 187–196 (2009)

    Article  Google Scholar 

  2. Sen, R.: Accurate speed and density measurement for road traffic in India. In: DEV 2013, Bangalore, India, 11–12 January 2013

    Google Scholar 

  3. Lee, J.: Estimation and analysis of urban traffic flow. In: ICIP (2009)

    Google Scholar 

  4. Sun, D.: From Pixels to Layers: Joint Motion Estimation and Segmentation, The Chinese University of Hong Kong (2007)

    Google Scholar 

  5. Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Fourth IEEE Workshop on Applications of Computer Vision, WACV 1998. Proceedings, pp. 8–14. IEEE (1998)

    Google Scholar 

  6. Liu, Y., Ai, H., Xu, G.: Moving object detection and tracking based on background subtraction. In: Multispectral Image Processing and Pattern Recognition, pp. 62–66. International Society for Optics and Photonics (2001)

    Google Scholar 

  7. Collins: Motion Detection in Static Backgrounds, SpringerBriefs in Computer Science. https://doi.org/10.1007/978-1-4471-4216-4_2,2012

  8. Bas, E.: Road and Traffic Analysis From Video, August 2007

    Google Scholar 

  9. Zhu, Z., Xu, G.: VISATRAM: a real-time vision system for automatic traffic monitoring. Image Vis. Comput. 18(10), 781–794 (2000)

    Article  Google Scholar 

  10. Sellent, A.: Optical Flow Estimation Versus Motion Estimation, August 2012

    Google Scholar 

  11. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)

    Article  Google Scholar 

  12. Sen-Ching, S.C.: Robust Techniques for Background Subtraction in Urban Traffic Video (2007)

    Google Scholar 

  13. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82(Series D), 35–45 (1960)

    Article  Google Scholar 

  14. Catlin. D.E.: Estimation, Control, and the Discrete Kalman Fillter. Springer, New York (1989)

    Chapter  MATH  Google Scholar 

  15. Mandellos, N.A., Keramitsoglou, I., Kiranoudis, C.T.: A background subtraction algorithm for detecting and tracking vehicles. Expert Syst. Appl. 38(3), 1619–1631 (2011)

    Article  Google Scholar 

  16. Unzueta, L., Nieto, M., Cortés, A., et al.: Adaptive multicue background subtraction for robust vehicle counting and classification. IEEE Trans. Intell. Transp. Syst. 13(2), 527–540 (2012)

    Article  Google Scholar 

  17. Pang, C.C.C., Lam, W.W.L., Yung, N.H.C.: A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images. IEEE Trans. Intell. Transp. 8(3), 441–459 (2007)

    Article  Google Scholar 

  18. Zhou, J., Gao, D., Zhang, D.: Moving vehicle detection for automatic traffic monitoring. IEEE Trans. Veh. Technol. 56(1), 51–59 (2007)

    Article  Google Scholar 

  19. Wu, B.F., Juang, J.H.: Adaptive vehicle detector approach for complex environments. IEEE Trans. Intell. Transp. Syst. 13(2), 817–827 (2012)

    Article  Google Scholar 

  20. Gangodkar, D., Kumar, P., Mittal, A.: Robust segmentation of moving vehicles under complex outdoor conditions. IEEE Trans. Intell. Transp. Syst. 13(4), 1738–1752 (2012)

    Article  Google Scholar 

  21. Chen, Z., Ellis, T., Velastin, S.A.: Vehicle detection, tracking and classification in urban traffic. In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, September 2012, pp. 951–956 (2012)

    Google Scholar 

  22. Tsai, L.W., Hsieh, J.W., Fan, K.C.: Vehicle detection using normalized color and edge map. IEEE Trans. Image Process. 16(3), 850–864 (2007)

    Article  MathSciNet  Google Scholar 

  23. Jia, Y., Zhang, C.: Front-view vehicle detection by Markov chain Monte Carlo method. Pattern Recognit. 42(3), 313–321 (2009)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, National University of Science and Technology, Islamabad, Pakistan

    Muhammad Abbas, Fozia Mehboob & Shoab A. Khan

  2. Department of Computer Science, Al-Imam Mohammed Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Muhammad Abbas, Fozia Mehboob & Abdul Rauf

  3. Department of Computer Science, Northumbria University, Newcastle upon Tyne, UK

    Muhammad Abbas, Fozia Mehboob & Richard Jiang

Authors
  1. Muhammad Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fozia Mehboob
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shoab A. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdul Rauf
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Richard Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdul Rauf .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Faculdade de Engenharia/LIACC, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abbas, M., Mehboob, F., Khan, S.A., Rauf, A., Jiang, R. (2019). Real Time Fuzzy Based Traffic Flow Estimation and Analysis. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_45

Download citation

Publish with us

Policies and ethics