Skip to main content

Traffic Surveillance for Smart City in Internet of Things Environment

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 869))

Abstract

In urban cities, there is an enormous increase of public and private vehicles. Due to significant rise in traffic, the high congestion and air pollution are observed. In real time, traffic surveillance is a challenging issue, which requires simultaneous monitoring and controlling. With the advancement in technology, this task is possible using Internet of Things, which provides a low cost, scalable and reliable solution. In this paper, we propose an IoT-based low cost and real time solution for vehicle counting and lane violation. A system was developed based on embedded IoT device using Raspberry Pi-3 and evaluated the proposed algorithm on it.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aslan, Y.E., Korpeoglu, I., Ulusoy, Ö.: A framework for use of wireless sensor networks in forest fire detection and monitoring. Comput. Environ. Urban Syst. 36(6), 614–625 (2012)

    Article  Google Scholar 

  2. Ollero, A., Merino, L.: Unmanned aerial vehicles as tools for forest-fire fighting. For. Ecol. Manage. 234(1), S263 (2006)

    Article  Google Scholar 

  3. 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 

  4. Bramberger, M., Brunner, J., Rinner, B., Schwabach, H.: Real-time video analysis on an embedded smart camera for traffic surveillance. In: Real-Time and Embedded Technology and Applications Symposium: 10th IEEE Proceedings. RTAS 2004, pp. 174–181. IEEE (2004)

    Google Scholar 

  5. Ihaddadene, N., Djeraba, C.: Real-time crowd motion analysis. In: 19th International Conference on Pattern Recognition, 2008. ICPR 2008, pp. 1–4. IEEE (2008)

    Google Scholar 

  6. Allan, W.: Aerial Surveillance and Fire-Control System 13 Feb 1973, US Patent 3,715,953

    Google Scholar 

  7. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol. 1, pp. 511–518. IEEE (2001)

    Google Scholar 

  8. Nascimento, J.C., Marques, J.S.: Performance evaluation of object detection algorithms for video surveillance. IEEE Trans. Multimedia 8(4), 761–774 (2006)

    Article  Google Scholar 

  9. Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Sirotti, S.: Improving shadow suppression in moving object detection with hsv color information. In: Intelligent Transportation Systems: Proceedings, pp. 334–339. IEEE (2001)

    Google Scholar 

  10. Tsai, C.-M.: Intelligent post-processing via bounding-box-based morphological operations for moving objects detection. Advanced Research in Applied Artificial Intelligence, pp. 647–657 (2012)

    Chapter  Google Scholar 

  11. Kim, K., Davis, L.: Object detection and tracking for intelligent video surveillance. Multimedia Analysis, Processing and Communications, pp. 265–288 (2011)

    Chapter  Google Scholar 

  12. Bayona, Á., SanMiguel, J.C., Martínez, J.M.: Stationary foreground detection using background subtraction and temporal difference in video surveillance. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4657–4660. IEEE (2010)

    Google Scholar 

  13. Zivkovic, Z., Van Der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)

    Article  Google Scholar 

  14. Lee, D.-S.: Effective gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005)

    Article  Google Scholar 

  15. Khurana, K., Awasthi, R.: Techniques for object recognition in images and multi-object detection. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(4), 1383 (2013)

    Google Scholar 

  16. Oji, R.: An Automatic Algorithm for Object Recognition and Detection Based on Asift Keypoints. arXiv preprint arXiv:1211.5829 (2012)

    Google Scholar 

  17. Xu, J., Zhang, C., Goto, S.: Object tracking by detection for video surveillance systems based on modified codebook foreground detection and particle filter. In: International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp. 1–6. IEEE (2011)

    Google Scholar 

  18. Zang, Q., Klette, R.: Object classification and tracking in video surveillance. In: Computer Analysis of Images and Patterns, pp. 198–205. Springer (2003)

    Google Scholar 

  19. Pyykönen, P., Laitinen, J., Viitanen, J., Eloranta, P., Korhonen, T.: Iot for intelligent traffic system. In: 2013 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 175–179. IEEE (2013)

    Google Scholar 

  20. Yu, M., Zhang, D., Cheng, Y., Wang, M.: An rfid electronic tag based automatic vehicle identification system for traffic iot applications. In: Control and Decision Conference (CCDC), Chinese, pp. 4192–4197. IEEE (2011)

    Google Scholar 

  21. Boskovich, S., Barth, M.: Vehicular network rerouting autonomy with a v2v, i2v, and v2i communication matrix classification. In: 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp. 172–177. IEEE (2013)

    Google Scholar 

  22. Jerbi, M., Marlier, P., Senouci, S.M.: Experimental assessment of v2v and i2v communications. In: IEEE International Conference on Mobile Adhoc and Sensor Systems: MASS 2007, pp. 1–6. IEEE (2007)

    Google Scholar 

  23. Ferryman, J.M., Maybank, S.J., Worrall, A.D.: Visual surveillance for moving vehicles. Int. J. Comput. Vis. 37(2), 187–197 (2000)

    Article  Google Scholar 

  24. Bhaskar, P.K., Yong, S.-P.: Image processing based vehicle detection and tracking method. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), pp. 1–5. IEEE (2014)

    Google Scholar 

  25. 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 

  26. Dallalzadeh, E., Guru, D.: Feature-based tracking approach for detection of moving vehicle in traffic videos. In: Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, pp. 254–260. ACM (2010)

    Google Scholar 

  27. Kluge, K., Lakshmanan, S.: A deformable-template approach to lane detection. In: Proceedings of the Intelligent Vehicles’ 95 Symposium, pp. 54–59. IEEE (1995)

    Google Scholar 

  28. Kim, Z.: Robust lane detection and tracking in challenging scenarios. IEEE Trans. Intell. Transp. Syst. 9(1), 16–26 (2008)

    Article  Google Scholar 

  29. Cheng, H.-Y., Jeng, B.-S., Tseng, P.-T., Fan, K.-C.: Lane detection with moving vehicles in the traffic scenes. IEEE Trans. Intell. Transp. Syst. 7(4), 571–582 (2006)

    Article  Google Scholar 

  30. Chandrajit, M., Girisha, R., Vasudev, T.: Multiple objects tracking in surveillance video using color and hu moments. Signal Image Process.: Int. J. 7, 15–27 (2016)

    Google Scholar 

  31. Ma, L., Qi, H., Zhu, S., Ma, S.: A fast background model based surveillance video coding in hevc. In: Visual Communications and Image Processing Conference, pp. 237–240. IEEE (2014)

    Google Scholar 

  32. Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition. ICPR 2004, vol. 2, pp. 28–31. IEEE (2004)

    Google Scholar 

  33. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  34. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graphics (TOG) 21(3), 257–266 (2002) ACM

    Google Scholar 

  35. California dmv - rules of the road 3 - traffic lanes. 2007 [Online] Available: https://www.youtube.com/watch?v=BC4-jYsJ9CQ&t=84s

  36. Joglekar, A.: Driving in india: Changing lanes and overtaking. 2007 [Online]. Available: https://www.youtube.com/watch?v=uAoA5A3QX04 (2007)

Download references

Acknowledgment

This work is supported by the Department of Electronics and Information Technology (DeiTY), funded by Ministry of Human Resource Development (MHRD), Government of India (Grant No. 13(4)/2016-CC&BT).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Meghavi Choksi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Choksi, M., Zaveri, M.A., Anand, S. (2019). Traffic Surveillance for Smart City in Internet of Things Environment. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_16

Download citation

Publish with us

Policies and ethics