Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network

Abstract

In this paper, we present an intelligent traffic congestion detection method using image classification approach on CCTV camera image feeds. We use a deep learning architecture, convolutional neural network (CNN) which is currently the state-of-the art for image processing method. We only do minimal image preprocessing steps on the small size image, where the conventional methods require a high quality, handcrafted features need to do manual calculation. The CNN model is trained to do binary classification about road traffic condition using 1000 CCTV monitoring image feeds with balance distribution. The result shows that a CNN with simple, basic architecture that trained on small grayscale images has an average classification accuracy of 89.50%.