Skip to main content
SpringerLink
Log in

Traffic event classification at intersections based on the severity of abnormality

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a novel traffic event classification approach using event severities at intersections. The proposed system basically learns normal and common traffic flow by clustering vehicle trajectories. Common vehicle routes are generated by implementing trajectory clustering with Continuous Hidden Markov Model. Vehicle abnormality is detected by observing maximum likelihoods of partial vehicle locations and velocities on underlying common route models. The second part of the work is based on extracting the severities of abnormality by deviation measurement using Coefficient of Variances method. By using abnormal event samples, two severity classes are built in order to recognize event severities by Support Vector Machines and k-Nearest Neighborhood algorithms. Experimental results show that the proposed model has high precision with satisfactory incident detection and event severity classification performance.

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

Access this article

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

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Cano, J., Kovaceva, J., Lindman, M., Brannstrom, M.: Automatic incident detection and classification at intersections, Volvo Car Corporation, 09-0245, Sweden (2009)

  2. Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. In: Proceedings of the IEEE, vol. 77, no. 2 (1989)

  3. TRIMARC, Traffic response and incident management assisting the river cities. http://www.trimarc.org

  4. NGSIM, Next Generation Simulation Community. http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm

  5. Morris B., Trivedi M.: A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1114–1127 (2008)

    Article  Google Scholar 

  6. Morris, B., Trivedi, M.: Learning and classification of trajectories in dynamic scenes: a general framework for live video analysis. In: IEEE 5th International Conference on Advanced Video and Signal Based Surveillance (2008)

  7. Piciarelli C., Foresti G.L.: Online trajectory clustering for anomalous events detection. Pattern Recognit. Lett. 27, 1835– 1842 (2006)

    Article  Google Scholar 

  8. Liao T.: Clustering of time series data. Pattern Recognit. 38, 1857–1874 (2005)

    Article  MATH  Google Scholar 

  9. Porikli, F.M.: Trajectory pattern detection by HMM parameter space features and eigenvector clustering. In: European Conference on Computer Vision (ECCV) (2004)

  10. Porikli, F.M.: Clustering variable length sequences by eigenvector decomposition using Hmm. In: International Workshop on Structural and Syntactic Pattern Recognition, vol. 3138/2004. ISSN:0307-9743 (2004)

  11. Fu, Z., Hu, W., Tan, T.: Similarity based vehicle trajectory clustering and anomaly detection. In: IEEE International Conference on Image Processing, September 2005

  12. Akoz, O., Karsligil, M.E.: Video-based traffic accident analysis at intersections using partial vehicle trajectories. IEEE International Conference on Image Processing, September 2010

  13. Akoz, O., Karsligil, M.E.: Severity detection of traffic accidents at intersections based on vehicle motion analysis and multiphase linear regression. In: IEEE International Conference on Intelligent Transportation Systems, September 2010

  14. Saligrama V., Konrad J., Jodoin P.: Video anomaly identifi- cation: a statistical approach. IEEE Signal Process. Mag. 27(5), 18 (2010)

    Article  Google Scholar 

  15. Saunier, N., Sayed, T., Lim, C.: Probabilistic collision prediction for vision-based automated road safety analysis. In: Intelligent Transportation Systems Conference, pp. 872–878. IEEE, Seattle (2007)

  16. Saunier, N., Sayed, T.: Clustering vehicle trajectories with Hidden Markov models. Application to automated traffic safety analysis. In: International Joint Conference on Neural Networks. IEEE, Vancouver (2006)

  17. Veeraraghavan H., Masoud O., Papanikolopoulos N.: Computer vision for intersection monitoring. IEEE Trans. Intell. Transp. Syst. 4(2), 78–89 (2003)

    Article  Google Scholar 

  18. Hu, W., Xiao, X., Xie, D., Tan, T: Traffic accident prediction using vehicle tracking and trajectory analysis. In: Proceedings of the IEEE Intelligent Transportation Systems (2003)

  19. Hu W., Xiao X., Xie D., Tan T., Maybank S.: Traffic accident prediction using 3-D model-based vehicle tracking. IEEE Trans. Veh. Technol. 53(3), 677 (2004)

    Article  Google Scholar 

  20. Hu W., Xiao X., Fu Z., Xie D., Tan T., Maybank S.: A system for learning statistical motion patterns. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1450–1464 (2006)

    Article  Google Scholar 

  21. Li, X.; Porikli, F.M.: A Hidden Markov Model framework for traffic event detection using video features. In: IEEE International Conference on Image Processing (ICIP), vol. 5, pp. 2901–2904. ISSN:1522-4880 (2004)

  22. Green, E., Agent, K.: Evaluation of Auto Incident Recording System. Kentucky Transportation Center, University of Kentucky (2001)

  23. Swears, E., A. Hoogs, A., Perera, A.G.A.: Learning motion patterns in surveillance video using HMM clustering. In: Proceedings of IEEE Visual Motion Computing (Motion) Workshop, Copper Mountain/Colarado (2008)

  24. Lin, C., Tai, J., Song, K.: Traffic monitoring based on real-time image tracking. In: Proceedings of the IEEE International Conference on Robotics & Automation (2003)

  25. Kamijo S., Matsushita Y., Ikeuchi K., Sakuchi M.: Traffic monitoring and accident detection at intersections. IEEE Trans. Intell. Transport. Syst. 1(2), 108 (2000)

    Article  Google Scholar 

  26. Ki Y., Lee D.: A traffic accident recording and reporting model at intersections. IEEE Trans. Intell. Transport. Syst. 8(2), 188 (2007)

    Article  Google Scholar 

  27. Zou, Y., Shi, G., Shi, H., Wang, Y.: Image sequences based traffic incident detection for signaled intersections using HMM. In: 9th International Conference on Hybrid Intelligent Systems (2009)

  28. Ikeda H., Matsuo, T., Kaneko, Y., Tsuji, K.: Abnormal incident detection system employing image processing technology. In: Proceedings of IEEE International Conference on Intelligent Transportation Systems (2009)

  29. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Computer Vision and Pattern Recognition, USA (1999)

  30. Fisher, R., Perkins, S., Walker, A., Wolfart, E.: Connected component labeling. http://homepages.inf.ed.ac.uk/rbf/HIPR2/label.htm (2003)

  31. Prati, A., Mikic, I., Trivedi, M., Cucchiara, R.: Detecting moving shadows: formulation, algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25 (2003)

  32. Cavallaro, A., Salvador, E., Ebrahimi, T.: Detecting shadows in image sequences. In: 1st European Conference on Visual Media Production (CVMP), pp. 165–174 (2005)

  33. Hu, J., Su, T., Jeng, S.: Robust background subtraction with shadow and highlight removal for indoor surveillance. IEEE International Conference on Intelligent Robots and Systems, pp. 4545–4550 (2007)

  34. Tomasi, C., ve Kanade, T.: Detection and tracking of point features. CMU Technical Report, CS 91-132 (1991)

  35. Suhr J.: Kanade-Lucas-Tomasi Feature Tracker. Yonsei University, Computer Vision Lab. (2009)

    Google Scholar 

  36. Alon, J., Sclaroff, S., Kollios, G., Pavlovic, V.: Discovering clusters in motion time-series data. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 375 (2003)

  37. Moon, T.: The expectation-maximization algorithm. IEEE Signal Processing Magazine (1996)

  38. Smyth P.: Clustering sequences with Hidden Markov Models. Adv. Neural Inf. Process. Syst. 9, 648 (1997)

    MathSciNet  Google Scholar 

  39. Hendricks W., Robey K.: The sampling distribution of the coefficient of variation. Ann. Math. Stat. 7(3), 129–132 (1936)

    Article  Google Scholar 

  40. Abdi H.: Coefficient of variation. Encyclopedia of Research Design, CA (2010)

    Google Scholar 

  41. Kohonen T.: Self-Organizating Maps. Springer, New York (1997)

    Book  Google Scholar 

  42. Gunn, S.: Support vector machines for classification and regression. Technical Report, Image Speech and Intelligent Systems Research, University of Southampton (1998)

  43. Gutierrez-Osuna, R.: Introduction to Pattern Analysis. Texas A&M University. http://courses.cs.tamu.edu/rgutier/ceg499_s02/

  44. Matlab, Bioinformatics Toolbox, Statistical Learning, SVM Tools. http://www.mathworks.ch/help/toolbox/bioinfo/ref/svmtrain.html

  45. Zou, Y., Shi, G., Shi, H., Zhao, H.: Traffic incident classification at intersections based on image sequences by HMM/SVM classifiers. Multimedia Tools and Applications (2010)

  46. Zisserman, A., Hartley, R.: Multiple View Geometry in Computer Vision (2004)

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Ömer Aköz & M. Elif Karsligil

Authors
  1. Ömer Aköz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Elif Karsligil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ömer Aköz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aköz, Ö., Karsligil, M.E. Traffic event classification at intersections based on the severity of abnormality. Machine Vision and Applications 25, 613–632 (2014). https://doi.org/10.1007/s00138-011-0390-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-011-0390-4

Keywords

Search

Navigation