On the Use of Bayesian Networks for Real-Time Urban Traffic Measurements: a Case Study with Low-Cost Devices

© 2020, Springer Science+Business Media, LLC, part of Springer Nature.This paper describes a low cost computer vision system able to obtain traffic metrics at urban intersections. The proposed system is based on a Bayesian network based reasoning model. It employs the data extracted from background subtraction and contrast analysis techniques applied to predefined regions of interest of the video sequences, to evaluate different traffic metrics. The system has been designed to be able to work with already installed urban cameras, in order to reduce installation costs. So, it can be configured to work with different types of image sizes and video frame rates, as well as to process images taken from different distances and perspectives. The validity of the proposed system has been proved using a Raspberry Pi platform and tested using two real surveillance video cameras managed by the local authority of Cartagena (Spain) during different environmental light conditions. Using this hardware the system is able to process VGA grayscale images at a rate of 8 frames per second.