Skip to main content
SpringerLink
Log in

Deep Learning Models for Moving Vehicle Detection in Traffic Surveillance Video for Smart City Applications

  • Conference paper
  • First Online:
Electronic Governance with Emerging Technologies (EGETC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1888))

  • 115 Accesses

Abstract

In the last few decades, CCTV surveillance has become a piece of unavoidable equipment in public places. The most important application is for traffic monitoring. Recently, accident cases have been rapidly increasing day by day. So, continuous analysis of vehicles is a challenging task. Developing an algorithm to find a specific vehicle or pedestrian is highly beneficial in such cases. Such a procedure can be done by video processing. Video processing and its applications have improved over the last few years. The advancements in this area are helpful in numerous fields. At the same time, the intervention of artificial intelligence also has a relevant role in this area. Machine learning has also become essential and valuable in various complex cases. Image classification using artificial intelligence provides a highly accurate result rather than any other classification method. This paper mainly compares different convolutional neural networks on moving objects detected by background subtraction and GMM with the morphological operation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

Institutional subscriptions

References

  1. Wikipedia.org/Video processing

    Google Scholar 

  2. Shaikh, S.H., Saeed, K., Chaki, N.: Moving Object detection using background subtraction. https://link.springer.com/book/10.1007/978-3-319-07386-6

  3. Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man, Cybern.—Part C: Appl. Rev. 34(3), pp. 334–352 (2004)

    Google Scholar 

  4. Karasulu, B., Korukoglu, S.: Performance Evaluation Software: Moving Object Detection and Tracking in Videos. Springer New York, New York, NY (2013)

    Book  Google Scholar 

  5. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), Article 13 (2006)

    Google Scholar 

  6. Ali, S.S., Zafar, M.F.: A robust adaptive method for detection and tracking of moving objects. In: International Conference on Emerging Technologies, pp. 262–266 (2009)

    Google Scholar 

  7. investopedia.com/terms/deep learning

    Google Scholar 

  8. wikipedia.org/wiki/Deep_learning

    Google Scholar 

  9. Risha, K.P., Kumar, A.C.: Novel Method of Detecting Moving Object in Video https://doi.org/10.1016/j.protcy.2016.05.235

  10. MATLAB/R2012b/help/ Clustering Using Gaussian Mixture Models

    Google Scholar 

  11. brilliant.org/wiki/gaussian-mixture-model

    Google Scholar 

  12. www.mathworks.com/help/images/morphological-dilation-and-erosion

  13. Digital Image Processing ,Rafel C Gonzalez, Richard E Woods

    Google Scholar 

  14. Tejada, E.D., Rodriguez, P.A.: Moving object detection videos using principle component pursuit and convolutional neural network. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 793–797 (2017). https://doi.org/10.1109/GlobalSIP.2017.8309069

  15. Kim, C., Lee, J., Han, T., Kim, Y.-M.: A hybrid framework combining background subtraction and deep neural networks for rapid person detection. J. Big Data 5(1), 1–24 (2018). https://doi.org/10.1186/s40537-018-0131-x

    Article  Google Scholar 

  16. Alvi, N.Z., Singh, K., Chandel, G., Varshney, Y.V.: Convolution neural network based real time moving object detection. Int. J. Adv. Sci. Technol. 29(10S), 8134–8143 (2020)

    Google Scholar 

  17. Kulchandani, J.S., Dangarwala, K.J.: Moving Object detection : Review of recent reseach trends. IEEE Xplore: 16 April 2015 ISBN:978–1–4799–6272–3

    Google Scholar 

  18. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of Convolutional Neural Networks. IEEE Xplore: 08 March (2018)

    Google Scholar 

  19. Chauhan, R., Ghanshala, K.K., Joshi, R.C.: Conolutional Neural Network for Image Detection and Recognition. IEEE Xplore: 02 May (2019)

    Google Scholar 

  20. Ajit, A., Acharya, K., Samanta, A.: A Review of Convolutional Neural Networks. IEEE Xplore: 27 April (2020)

    Google Scholar 

  21. https://www.jpjodoin.com/urbantracker/dataset.html

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    K. Risha & D. Jude Hemanth

Authors
  1. K. Risha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Jude Hemanth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Jude Hemanth .

Editor information

Editors and Affiliations

  1. Tamaulipas Autonomous University, Reynosa, Tamaulipas, Mexico

    Fernando Ortiz-Rodríguez

  2. Autonomous University of Tamaulipas, Reynosa, Tamaulipas, Mexico

    Sanju Tiwari

  3. University of Uyo, Uyo, Nigeria

    Patience Usoro Usip

  4. Poznan Supercomputing and Networking Center, Poznan, Poland

    Raul Palma

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Risha, K., Jude Hemanth, D. (2023). Deep Learning Models for Moving Vehicle Detection in Traffic Surveillance Video for Smart City Applications. In: Ortiz-Rodríguez, F., Tiwari, S., Usoro Usip, P., Palma, R. (eds) Electronic Governance with Emerging Technologies. EGETC 2023. Communications in Computer and Information Science, vol 1888. Springer, Cham. https://doi.org/10.1007/978-3-031-43940-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43940-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43939-1

  • Online ISBN: 978-3-031-43940-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation