Cluster Computing

, Volume 21, Issue 1, pp 135–147 | Cite as

Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

  • V. C. Maha VishnuEmail author
  • M. Rajalakshmi
  • R. Nedunchezhian


Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.


Intelligent traffic video surveillance Hybrid support vector machine Histogram of flow gradient Multinomial logistic regression Dynamic traffic signal control 


  1. 1.
    Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res. Part C: Emerg. Technol. 6(4), 271–288 (1998)CrossRefGoogle Scholar
  2. 2.
    Ondrej, M., Zboril Frantisek, V., Martin, D.: Algorithmic and mathematical principles of automatic number plate recognition systems. Brno University of technology, 10 (2007)Google Scholar
  3. 3.
    Kranthi, S., Pranathi, K., Srisaila, A.: Automatic number plate recognition. Int. J. Adv. Technol. 2(3), 408–422 (2011)Google Scholar
  4. 4.
    Chang, S.L., Chen, L.S., Chung, Y.C., Chen, S.W.: Automatic license plate recognition. IEEE Trans. Intell. Transp. syst. 5(1), 42–53 (2004)CrossRefGoogle Scholar
  5. 5.
    Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011)CrossRefGoogle Scholar
  6. 6.
    Qadri, M.T., Asif, M.: Automatic number plate recognition system for vehicle identification using optical character recognition. In: 2009 International Conference on Education Technology and Computer, pp. 335–338. IEEE (2009)Google Scholar
  7. 7.
    Kaminer, I., Pascoal, A., Hallberg, E., Silvestre, C.: Trajectory tracking for autonomous vehicles: an integrated approach to guidance and control. J. Guid. Control Dyn. 21(1), 29–38 (1998)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kamble, S., Godbole, C., Gaikwad, R., Yadav, A.P.: Vehicle tracking system. Int. J. Innov. Res. Sci. Technol. 2(11), 415–419 (2016)Google Scholar
  9. 9.
    Srinivasan, S., Latchman, H., Shea, J., Wong, T., McNair, J.: Airborne traffic surveillance systems: video surveillance of highway traffic. In: Proceedings of the ACM 2nd International Workshop on Video surveillance & Sensor Networks, pp. 131–135. ACM (2004)Google Scholar
  10. 10.
    Kwak, C., Clayton-Matthews, A.: Multinomial logistic regression. Nurs. Res. 51(6), 404–410 (2002)CrossRefGoogle Scholar
  11. 11.
    Wenjie, C., Lifeng, C., Zhanglong, C., Shiliang, T.: A realtime dynamic traffic control system based on wireless sensor network. In: 2005 International Conference on Parallel Processing Workshops (ICPPW’05), pp. 258–264. IEEE (2005)Google Scholar
  12. 12.
    Kamijo, S., Matsushita, Y., Ikeuchi, K., Sakauchi, M.: Traffic monitoring and accident detection at intersections. IEEE Trans. Intell. Transp. Syst. 1(2), 108–118 (2000)CrossRefGoogle Scholar
  13. 13.
    Li, X., Porikli, F.M.: A hidden Markov model framework for traffic event detection using video features. In: 2004 International Conference on Image Processing, 2004. ICIP’04, vol. 5, pp. 2901–2904. IEEE (2004)Google Scholar
  14. 14.
    Smith, S.M.: ASSET-2: real-time motion segmentation and shape tracking. In: Proceedings of the Fifth International Conference on Computer Vision, pp. 237–244. IEEE (1995)Google Scholar
  15. 15.
    Wixson, L.: Detecting salient motion by accumulating directionally-consistent flow. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 774–780 (2000)CrossRefGoogle Scholar
  16. 16.
    Koller, D., Weber, J., Haung, T., Malik, J., Ogasawara, G., Rao, B., Russel, S.: Towards robust automatic traffic scene analysis in realtime. In: Proceedings of the 12th International Conference on Pattern Recognition (ICPR-94), pp. 126–131 (1994)Google Scholar
  17. 17.
    Kanhere, N., Birchfield, S., Sarasua, W.: Vehicle segmentation and tracking in the presence of occlusions. Transp. Res. Rec.: J. Transp. Res. Board 1944, 89–97 (2006)CrossRefGoogle Scholar
  18. 18.
    Choi, J.Y., Sung, K.S., Yang, Y.K.: Multiple vehicles detection and tracking based on scale-invariant feature transform. In: 2007 IEEE Intelligent Transportation Systems Conference, pp. 528–533. IEEE (2007)Google Scholar
  19. 19.
    Chachich, A.C., Pau, A., Barber, A., Kennedy, K., Olejniczak, E., Hackney, J., Mireles, E.: Traffic sensor using a color vision method. In: International Society for Optics and Photonics, pp. 156–165 (1997)Google Scholar
  20. 20.
    Green, E.R., Agent, K.R., Pigman, J.G.: Evaluation of auto incident recording system (AIRS) (2005)Google Scholar
  21. 21.
    Lai, A.H.S., Yung, N.H.C.: A video-based system methodology for detecting red light runners. In: Proceedings IAPR Workshop on MVA’98, pp. 23–26 (1998)Google Scholar
  22. 22.
    Ikeda, H., Kaneko, Y., Matsuo, T., Tsuji, K.: Abnormal incident detection system employing image processing technology. In: Proceedings of the 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp. 748–752. IEEE (1999)Google Scholar
  23. 23.
    Kimachi, M., Kanayama, K., Teramoto, K.: Incident prediction by fuzzy image sequence analysis. In: Proceedings of the Vehicle Navigation and Information Systems Conference, pp. 51–56. IEEE (1994)Google Scholar
  24. 24.
    Trivedi, M.M., Mikic, I., Kogut, G.: Distributed video networks for incident detection and management. In: Proceedings of IEEE Int’l Conference on Intelligent Transportation Systems, pp. 155–160 (2000)Google Scholar
  25. 25.
    Atev, S., Arumugam, H., Masoud, O., Janardan, R., Papanikolopoulos, N.P.: A vision-based approach to collision prediction at traffic intersections. IEEE Trans. Intell. Transp. Syst. 6(4), 416–423 (2005)CrossRefGoogle Scholar
  26. 26.
    Hu, W., Xiao, X., Xie, D., Tan, T.: Traffic accident prediction using vehicle tracking and trajectory analysis. In: Proceedings of the 2003 IEEE Intelligent Transportation Systems, vol. 1, pp. 220–225. IEEE (2003)Google Scholar
  27. 27.
    Ki, Y.K., Lee, D.Y.: A traffic accident recording and reporting model at intersections. IEEE Trans. Intell. Transp. Syst. 8(2), 188–194 (2007)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Rakesh, M., Ajeya, B., Mohan, A.R.: Hybrid median filter for impulse noise removal of an image in image restoration. Impulse 2(10), 5117–5124 (2013)Google Scholar
  29. 29.
    Samantaray, S.R., Dash, P.K.: High impedance fault detection in distribution feeders using extended kalman filter and support vector machine. Int. Trans. Electr. Energy Syst. 20(3), 382–393 (2010)Google Scholar
  30. 30.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  31. 31.
    Xiang, J., Chen, Z.: An adaptive traffic signal coordination optimization method based on vehicle-to-infrastructure communication. Clust. Comput. 19(3), 1503–1514 (2016)CrossRefGoogle Scholar
  32. 32.
    Zhuo, T.: Face recognition from a single image per person using deep architecture neural networks. Clust. Comput. 19(1), 73–77 (2016)CrossRefGoogle Scholar
  33. 33.
    Zheng, W., Wang, Z., Huang, H., Meng, L., Qiu, X.: SPSRG: a prediction approach for correlated failures in distributed computing systems. Clust. Comput. 19(4), 1703–1721 (2016)CrossRefGoogle Scholar
  34. 34.
    Kang, S., Kim, T., Jeon, H., Lee, W., Kang, S.: A healthcare information sharing scheme in distributed cloud networks. Clust. Comput. 18(4), 1405–1410 (2015)CrossRefGoogle Scholar
  35. 35.
    Ki, Y.K.: Accident detection system using image processing and MDR. Int. J. Comput. Sci. Netw. Secur. IJCSNS 7(3), 35–39 (2007)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • V. C. Maha Vishnu
    • 1
    Email author
  • M. Rajalakshmi
    • 1
  • R. Nedunchezhian
    • 1
  1. 1.Department of Computer Science and Engineering & Information TechnologyCoimbatore Institute of TechnologyCoimbatoreIndia

Personalised recommendations