Road Pollution Estimation Using Static Cameras And Neural Networks
Miguel A. Molina‐Cabello, Rafael Marcos Luque‐Baena, Ezequiel López‐Rubio, Lipika Deka, Karl Thurnhofer‐Hemsi · 2018
This paper presents a methodology for estimating pollution on roads by analyzing traffic video sequences. The objective is to take advantage of the huge network of static cameras which is possible to lind in the road system of any state or country to estimate the pollution on each area. This proposal uses deep learning neural networks for the object detection, and a pollution estimation model based on the frequency of vehicles and their speed. The experiments show promising results which suggest that the system can be used alone or combined with existing systems for measuring pollution on roads.