Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach

• URL: http://arxiv.org/abs/2209.07943v1
• Date: Fri, 16 Sep 2022 14:02:20 GMT
• Title: Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach
• Authors: Mirza Fuad Adnan, Nadim Ahmed, Imrez Ishraque, Md. Sifath Al Amin, Md. Sumit Hasan
• Abstract summary: This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The traffic video data has become a critical factor in confining the state of traffic congestion due to the recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a Deep Convolutional Neural Network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%.

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