Skip to main content
SpringerLink
Log in

Road scene analysis for determination of road traffic density

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Villagra J, Milanes V, Perez J, Godoy J. Smooth path and speed planning for an automated public transport vehicle. Robotics and Autonomous Systems, 2012, 60: 252–26

    Article  Google Scholar 

  2. Towards a new culture for urban mobility. Green paper of European Commission, 2007, 1–6

  3. Hartgen D, Fields M G, Moore A. Gridlock and growth: the effect of traffic congestion on regional economic performance. Reason Foundation, 2009, 1–8

    Google Scholar 

  4. Schrank D, Lomax T, Eisele B. 2011 Urban Mobility Report and Appendices. Texas Transportation Institute, 2011

    Google Scholar 

  5. Lomax T, Turner S, Shunk G, Levinson H S, Pratt R H, Bay P N, Douglas G B. Quantifying congestion. Transportation Research Board, 1997

    Google Scholar 

  6. Pattara-Atikom M, Pongpaibool P, Thajchayapong S. Estimating road traffic congestion using vehicle velocity. In: Proceedings of the 6th International Conference on ITS Telecommunications Proceedings. 2006, 1001–1004

    Google Scholar 

  7. Oh J, Min J, Kim M, Cho H. Development of an automatic traffic conflict detection system based an image tracking technology. Transportation Research Record, 2009, 45–54

    Google Scholar 

  8. Cheng H Y, Hsu S H. Intelligent highway traffic surveillance with self-diagnosis abilities. IEEE Transactions on Intelligent Transportation Systems, 2011, 12: 1462–1472

    Article  Google Scholar 

  9. Houben Q, Diaz J C T, Warzee N, Debeir O, Czyz J, Insticc. Multifeature stereo vision system for road traffic analysis. In: Proceedings of the 4th International Conference on Computer Vision Theory and Applications. 2009, 554–559

    Google Scholar 

  10. Bishop B, Casabona J. Automatic congestion detection and visualization using networked GPS unit data. In: Proceedings of the 47th Annual Southeast Regional Conference. 2009, 79

    Google Scholar 

  11. Ozkurt C, Camci F. Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Mathematical&Computational Applications, 2009, 14: 187–196

    Google Scholar 

  12. Jain V, Sharma A, Subramanian L. Road traffic congestion in the developing world. In: Proceedings of the 2nd ACM Symposium on Computing for Development. 2012, 11

    Google Scholar 

  13. Chen Y L, Wu B F, Huang H Y, Fan C J. A real-time vision system for nighttime vehicle detection and traffic surveillance. IEEE Transactions on Industrial Electronics, 2011, 58: 2030–2044

    Article  Google Scholar 

  14. Yu Z, Chen Y P. A real-time motion detection algorithm for traffic monitoring systems based on consecutive temporal difference. In: Proceedings of the 7th Asian Control Conference. 2009, 1594–1599

    Google Scholar 

  15. Marfia G, Roccetti M. Vehicular congestion detection and short-term forecasting: a new model with results. IEEE Transactions on Vehicular Technology, 2011, 60: 2936–2948

    Article  Google Scholar 

  16. Brahme Y B, Kulkarni P S. An implementation of moving object detection, tracking and counting objects for traffic surveillance system. In: Proceedings of the 2011 International Conference on Computational Intelligence and Communication Networks. 2011, 143–148

    Chapter  Google Scholar 

  17. Buch N, Velastin S A, Orwell J. A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Systems, 2011, 12: 920–939

    Article  Google Scholar 

  18. Al-Kadi O S. Combined statistical and model based texture features for improved image classification. In: Proceedings of the 4th International Conference on Advances in Medical, Signal and Information Processing. 2008, 1–4

    Google Scholar 

  19. Al-Kadi O S. Supervised texture segmentation: a comparative study. In: Proceedings of the 2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies. 2012, 1–5

    Google Scholar 

  20. Gonzales R C, Woods R E. Digital Image Processing. Prentice Hall, 2002, 299–300

    Google Scholar 

  21. Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24: 971–987

    Article  Google Scholar 

  22. Guo Y, Zhao G, Pietik M. Discriminative features for texture description. Pattern Recognition, 2012, 45: 3834–3843

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. King Abdullah II School for Information Technology, University of Jordan, Amman, 11942, Jordan

    Omar Al-Kadi, Rizik Al-Sayyed & Ja’far Alqatawna

  2. College of Engineering and Computer Science, Australian National University, Canberra, ACT, 0200, Australia

    Osama Al-Kadi

Authors
  1. Omar Al-Kadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Osama Al-Kadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rizik Al-Sayyed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ja’far Alqatawna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omar Al-Kadi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Kadi, O., Al-Kadi, O., Al-Sayyed, R. et al. Road scene analysis for determination of road traffic density. Front. Comput. Sci. 8, 619–628 (2014). https://doi.org/10.1007/s11704-014-3156-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-014-3156-0

Keywords

Search

Navigation