Efficient Traffic Density Estimation Using Convolutional Neural Network

Abstract

Measuring traffic density is a problem which has been extensively worked upon by the Computer Vision community. Various solutions have been proposed over the years which have surely attributed to the deepening of the field of image analysis but the most significant improvements have been made in recent years after the huge advancements in the area of Deep neural networks. In this paper, an efficient convolutional neural network has been proposed to estimate the traffic density. For the same, a new dataset of labeled images are generated from openly available traffic video footage. The method is compared with state-of-the-art method in terms of accuracy and loss. The results show the significant improvement in the traffic density estimation.