Advertisement

Cluster Computing

, Volume 21, Issue 1, pp 443–452 | Cite as

Trajectory based vehicle counting and anomalous event visualization in smart cities

  • Fozia MehboobEmail author
  • Muhammad Abbas
  • Richard Jiang
  • Abdul Rauf
  • Shoab A. Khan
  • Saad Rehman
Article
  • 297 Downloads

Abstract

Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.

Keywords

Motion tracking Surveillance videos Density estimation Pattern analysis 

References

  1. 1.
    Jiang, F., Tsaftaris, S.A., Wu, Y., Katsaggelos, A.K.: Detecting anomalous trajectories from highway traffic data (2009)Google Scholar
  2. 2.
    Ivanov, Y.A., Bobick, A.F.: Recognition of visual activities and interactions by stochastic parsing. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 852–872 (2000)CrossRefGoogle Scholar
  3. 3.
    Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)CrossRefGoogle Scholar
  4. 4.
    Haritaoglu, I., Harwood, D., David, L.S.: W4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 809–830 (2000)CrossRefGoogle Scholar
  5. 5.
    Todd, N., Schoepflin, N., Dailey, D.J.: Dynamic camera calibration of roadside traffic estimation management cameras for vehicle speed. IEEE Trans. Intell. Transp. Syst. 4(2), 90–98 (2003)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Sun, P., Chawla, S.: On local spatial outliers. In: ICDM, pp. 209–216 (2004)Google Scholar
  8. 8.
    Li, X., Li, Z., Han, J., Lee, J.-G.: Temporal outlier detection in vehicle traffic data. In: ICDE, pp. 1319–1322 (2009)Google Scholar
  9. 9.
    Piciarelli, C., Foresti, G.L.: On-line trajectory clustering for anomalous events detection. Pattern Recogn. Lett. 27(15), 1835–1842 (2006). doi: 10.1016/j.patrec.2006.02.004 CrossRefGoogle Scholar
  10. 10.
    Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008). doi: 10.1109/TCSVT.2008.2005599 CrossRefGoogle Scholar
  11. 11.
    Patino, L., Ferryman, J., Beleznai, C.: Abnormal behaviour detection on queue analysis from stereo cameras. In: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2015)Google Scholar
  12. 12.
    Cosar, S., Donatiello, G., Bogorny, V., Garate, C., Alvares, L.O., Bremond, F.: Towards abnormal trajectory and event detection in video surveillance. IEEE Trans. Circuits Syst. Video Technol. 27, 683–695 (2016)CrossRefGoogle Scholar
  13. 13.
    Chen, C., Zhang, D., Castro, P.S., Li, N., Sun, L., Li, S.: Real-time detection of anomalous taxi trajectories from GPS traces. In: International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, pp. 63–74. Springer, Berlin (2011)Google Scholar
  14. 14.
    Kruthiventi, S.S., Venkatesh Babu, R.: Dominant flow extraction and analysis in traffic surveillance videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 63–71 (2015)Google Scholar
  15. 15.
    Lan, J., Long, C., Wong, R.C.W., Chen, Y., Fu, Y., Guo, D., Li, J.: A new framework for traffic anomaly detection. In: SDM, pp. 875–883 (2014)Google Scholar
  16. 16.
    Hsieh, C.T., Hsu, S.B., Han, C.C., Fan, K.C.: Abnormal event detection using trajectory features. J. Inf. Technol. Appl. 5(1), 22–27 (2011)Google Scholar
  17. 17.
    Jiang, F., Yuan, J., Tsaftaris, S.A., Katsaggelos, A.K.: Anomalous video event detection using spatiotemporal context. Comput. Vis. Image Underst. 115(3), 323–333 (2011)CrossRefGoogle Scholar
  18. 18.
    Faraway, Julian J., Reed, Matthew P., Wang, Jing: Modelling three-dimensional trajectories by using Bézier curves with application to hand motion. J. R Stat. Soc. C 56(5), 571–585 (2007)CrossRefGoogle Scholar
  19. 19.
    Makris, D., Ellis, T.: Path detection in video surveillance. Image Vis. Comput. 20(12), 895–903 (2002)CrossRefGoogle Scholar
  20. 20.
    Bennewitz, M., Burgard, W., Cielniak, G.: Utilizing learned motion patterns to robustly track persons. In: Proceeding IEEE International Workshop Visual Surveillance Perform. Evaluation of Tracking and Surveillance, pp. 102–109 (2003)Google Scholar
  21. 21.
    Morris, B.T., Trivedi, M.M.: Learning, modeling, and classification of vehicle track patterns from live video. IEEE Trans. Intell. Transp. Syst. 9(3), 425–437 (2008)Google Scholar
  22. 22.
    Duffy, B., et al.: Glyph-based video visualization for semen analysis. IEEE Trans. Vis. Comput. Gr. 21(8), 980–993 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Fozia Mehboob
    • 1
    • 2
    Email author
  • Muhammad Abbas
    • 1
  • Richard Jiang
    • 2
  • Abdul Rauf
    • 3
  • Shoab A. Khan
    • 1
  • Saad Rehman
    • 1
  1. 1.National University of Sciences & TechnologyIslamabadPakistan
  2. 2.Northumbria University of Digital Science & TechnologyNewcastle Upon TyneUK
  3. 3.Al-Imam Muhammad ibn Saud Islamic UniversityRiyadhSaudi Arabia

Personalised recommendations