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Optical and Wireless Technologies pp 137–145Cite as

Traffic Control Enhancement with Video Camera Images Using AI

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 648))

Abstract

Traffic congestion has been an emerging issue when it comes to problems faced by commuters on road on a daily basis. It leads to loss of time, money, and fuel when one is stuck in a traffic jam. This has led to the need of more path-breaking technologies in the field of intelligent transport systems (ITS). Today, a lot of data are available which can be used to extract important information and perform the desired analysis. With CCTV surveillance cameras at almost every traffic pole, information like count of vehicles can be used to analyze the traffic patterns at a particular location. In this paper, different methods have been used to get the accurate count of vehicles and their performances have been analyzed. Popular image processing method background subtraction and deep learning algorithms: R-CNN, Fast R-CNN and Faster R-CNN have been implemented.

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References

  1. Lee W, Lee M (2010) A multi-class object classifier using boosted gaussian mixture model. Neural information processing. Theory and algorithms. Lecture notes in computer science, vol 6443. Springer, Berlin, Heidelberg, pp 430–437

    Google Scholar 

  2. Xue G, Sun J, Song L (2012) Background subtraction based on phase feature and distance transform. J Pattern Recognit Lett 33:1601–1613

    Article  Google Scholar 

  3. Wren C, Azarbayejani A, Darrell T, Pentland A (1996) Pfinder: real-time tracking of the human body. In: Proceedings of the second international conference on automatic face and gesture recognition, pp 51–56

    Google Scholar 

  4. Friedman N, Russell S (1997) Image segmentation in video sequences: a probabilistic approach. In: Proceeding UAI’97: proceedings of the thirteenth conference on uncertainty in artificial intelligence, pp 175–181

    Google Scholar 

  5. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: 1999 conference on computer vision and pattern recognition (CVPR ’99), pp 23–25

    Google Scholar 

  6. Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. NIPS

    Google Scholar 

  7. LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L (1989) Back propagation applied to handwritten zip code recognition. Neural Comput 1:541–551

    Article  Google Scholar 

  8. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR

    Google Scholar 

  9. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) OverFeat: integrated recognition localization and detection using convolutional networks. In: ICLR

    Google Scholar 

  10. Ren S, He K, Girshick R, Sun J, Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R (2015) Faster R-CNN: towards real time object detection with region proposal networks. Advances in neural information processing systems, vol 28. Curran Associates, Inc., New York, pp 91–99

    Google Scholar 

  11. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, UC Berkeley

    Google Scholar 

  12. Nurhadiyatna A, Jatmiko W, Hardjono B (2013) Background subtraction using Gaussian mixture model enhanced by hole filling algorithm (GMMHF). In: 2013 IEEE international conference on systems, man, and cybernetics

    Google Scholar 

  13. Uijlings JRR, van de Sande KEA, Gevers T, Smeulders AWM (2012) Selective search for object recognition. Technical report, University of Trento, Italy/University of Amsterdam, the Netherlands

    Google Scholar 

  14. Girshick R (2015) Fast R-CNN. In: 2015 IEEE international conference on computer vision

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, School of Engineering, Shiv Nadar University, Greater Noida, India

    Kriti Singh & P. C. Jain

Authors
  1. Kriti Singh
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  2. P. C. Jain
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Corresponding author

Correspondence to Kriti Singh .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Vijay Janyani

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Ghanshyam Singh

  3. Department of Electronics and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Manish Tiwari

  4. School of Engineering and Applied Sciences, Nile University, Cairo, Egypt

    Tawfik Ismail

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Cite this paper

Singh, K., Jain, P.C. (2020). Traffic Control Enhancement with Video Camera Images Using AI. In: Janyani, V., Singh, G., Tiwari, M., Ismail, T. (eds) Optical and Wireless Technologies. Lecture Notes in Electrical Engineering, vol 648. Springer, Singapore. https://doi.org/10.1007/978-981-15-2926-9_16

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  • DOI: https://doi.org/10.1007/978-981-15-2926-9_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2925-2

  • Online ISBN: 978-981-15-2926-9

  • eBook Packages: EngineeringEngineering (R0)

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