Congestion Control early warning system using Deep Learning

Sandeep N; Ragul N.S; Nikil Dhas P; Vaishnavi V

doi:10.34256/ijcci2124

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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