Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks

Abstract

Air quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to design strategies that may make the pollution levels to be reduced. In this work, a method which detects the pollution levels of transport vehicles from the images of IP cameras by means of computer vision techniques and neural networks is proposed. Specifically, for each sequence of images, a homography is calculated to correct the camera perspective and determine the real distance for each pixel. Subsequently, the trajectory of each vehicle is computed by applying convolutional neural networks for object detection and tracking algorithms. Finally, the speed in each frame and the pollution emitted by each vehicle are determined. Experimental results on several datasets available in the literature support the feasibility and scalability of the system as an emission control strategy.