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

Abstract

Traffic video data has become a critical factor in limiting traffic congestion due to 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, and 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 network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%