Skip to main content

Advertisement

Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

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.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  2. Ondrej, M., Zboril Frantisek, V., Martin, D.: Algorithmic and mathematical principles of automatic number plate recognition systems. Brno University of technology, 10 (2007)

  3. Kranthi, S., Pranathi, K., Srisaila, A.: Automatic number plate recognition. Int. J. Adv. Technol. 2(3), 408–422 (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  MATH  Google Scholar 

  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. 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)

  10. Kwak, C., Clayton-Matthews, A.: Multinomial logistic regression. Nurs. Res. 51(6), 404–410 (2002)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  15. Wixson, L.: Detecting salient motion by accumulating directionally-consistent flow. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 774–780 (2000)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  20. Green, E.R., Agent, K.R., Pigman, J.G.: Evaluation of auto incident recording system (AIRS) (2005)

  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)

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  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)

  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)

    Article  MathSciNet  Google Scholar 

  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. 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. 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)

  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)

    Article  Google Scholar 

  32. Zhuo, T.: Face recognition from a single image per person using deep architecture neural networks. Clust. Comput. 19(1), 73–77 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. C. Maha Vishnu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maha Vishnu, V.C., Rajalakshmi, M. & Nedunchezhian, R. Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control. Cluster Comput 21, 135–147 (2018). https://doi.org/10.1007/s10586-017-0974-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0974-5

Keywords

Navigation