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Title: Automated Traffic Light Signal Violation Detection System Using Convolutional Neural Network
Authors: Bordia B.
Nishanth N.
Patel S.
Anand Kumar M.
Rudra B.
Issue Date: 2020
Citation: Advances in Intelligent Systems and Computing , Vol. 1154 , , p. 579 - 592
Abstract: Automated traffic light violation detection system relies on the detection of traffic light color from the video captured with the CCTV camera, detection of the white safety line before the traffic signal and vehicles. Detection of the vehicles crossing traffic signals is generally done with the help of sensors which get triggered when the traffic signal turns red or yellow. Sometimes, these sensors get triggered even when the person crosses the line or some animal crossover or because of some bad weather that gives false results. In this paper, we present a software which will work on image processing and convolutional neural network to detect the traffic signals, vehicles and the white safety line present in front of the traffic signals. We present an efficient way to detect the white safety line in this paper combined with the detection of traffic lights trained on the Bosch dataset and vehicle detection using the TensorFlow object detection SSD model. © 2020, Springer Nature Singapore Pte Ltd.
Appears in Collections:2. Conference Papers

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