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

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 convolutio...

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Bibliographic Details
Main Authors: Adnan, Mirza Fuad, Ahmed, Nadim, Ishraque, Imrez, Amin, Md. Sifath Al, Hasan, Md. Sumit
Format: Conference Proceeding
Language:English
Subjects:
Deep Convolutional Neural Network
Image color analysis
Image segmentation
Roads
Space vehicles
Traffic Congestion
Training
Transforms
Vehicle detection
YOLO
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Summary: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%.
ISSN:2831-3682
DOI:10.1109/ICEET56468.2022.10007425