RT Journal Article
T1 Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks
A1 García-González, Jorge
A1 Molina-Cabello, Miguel Ángel
A1 Luque-Baena, Rafael Marcos
A1 Ortiz-de-Lazcano-Lobato, Juan Miguel
A1 López-Rubio, Ezequiel
K1 Aire - Contaminación
AB 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.
PB Elsevier
YR 2021
FD 2021-12
LK https://hdl.handle.net/10630/23957
UL https://hdl.handle.net/10630/23957
LA eng
NO García-González, Jorge ; Molina Cabello, Miguel Ángel ; Luque-Baena, Rafael ; Ortiz-de-lazcano-Lobato, Juan Miguel ; López-Rubio, Ezequiel. Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks.Applied Soft Computing Volume 113, Part B, December 2021, 107950.  https://doi.org/10.1016/j.asoc.2021.107950
NO This work is partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, roject name ‘‘Automated detection with low-cost hardware of unusual activities n video sequences’’. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name ‘‘Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems’’. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_01, project name ‘‘Anomaly detection on roads by moving cameras’’, and B1-2019_02, project name ‘‘Self-OrganizingNeural Systems for Non-Stationary Environments’’. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga.thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputingand Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Finally, the authors thankfullyacknowledge the grant of the Universidad de Málaga and the Instituto de Investigación Biomédica de Málaga - IBIMA. Funding for Open Access charge: University of Málaga/CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 1 feb 2026