Congestion Control early warning system using Deep Learning

  • Sandeep N Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
  • Ragul N.S Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
  • Nikil Dhas P Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
  • Vaishnavi V Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
Keywords: Congestion, Object Tracking, Faster R-CNN, MATLAB, Accuracy, Cloud Storage

Abstract

A new approach is proposed to analyze the live crowd and to provide an alert at the time of congestion, over-crowding and sudden gathering of pedestrians in a particular region. This paper proposes a completely software-oriented approach using MATLAB where it uses object detection and object tracking using Faster R- CNN (Region Based Convolutional Neural Network) algorithm where inception model of Google is used as CNN model which is pre-trained. This proposed method gives significant result on proposed dataset and the crowd congestion using Faster R-CNN approach which gives an accuracy of 93.503% at the rate 28 frames per second and the crowd detected video frames are uploaded to cloud storage.

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References

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https://en.wikipedia.org/wiki/2017_Mumbai_stamped e, as on 29th Oct., 2018.

Published
2021-10-30
How to Cite
N, S., N.S, R., P, N. D., & V, V. (2021). Congestion Control early warning system using Deep Learning. International Journal of Computer Communication and Informatics, 3(2), 35-50. https://doi.org/10.34256/ijcci2124



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