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

Mirza Fuad Adnan, Nadim Ahmed, Imrez Ishraque, Md. Sifath Al Amin, Md. Sumit Hasan

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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