RT Journal Article
T1 Detection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent size
A1 García Aguilar, Iván
A1 García-González, Jorge
A1 Medina, Daniel
A1 Luque-Baena, Rafael Marcos
A1 Domínguez-Merino, Enrique
A1 López-Rubio, Ezequiel
K1 Lingüística computacional
K1 Aprendizaje automático (Inteligencia artificial)
K1 Informática móvil
AB Autonomous driving requires information such as the velocity of other vehicles to prevent potential hazards. This work proposes a real-time deep learning-based framework to estimate vehicle speeds from image captures through an onboard camera. Vehicles are detected and tracked by the proposed deep neural networks and a tracking algorithm, which analyzes the trajectories. Finally, a linear regression model estimates the speed of a vehicle based on its position and size in the camera frame. This proposal has been tested with two sequences of the Prevention dataset with satisfactory results. The system can estimate the speed of multiple vehicles simultaneously. It can be integrated easily with onboard computer systems, thus allowing to development of a low-cost solution for speed estimation in an everyday vehicle. The potential applications include vehicle safety systems, driver assistance, and autonomous driving technologies.
PB Elsevier
YR 2023
FD 2023-11-17
LK https://hdl.handle.net/10630/28843
UL https://hdl.handle.net/10630/28843
LA spa
NO Iván García-Aguilar, Jorge García-González, Daniel Medina, Rafael Marcos Luque-Baena, Enrique Domínguez, Ezequiel López-Rubio, Detection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent size, Neurocomputing, Volume 567, 2024, 127057, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2023.127057
NO Funding for open Access charge: Universidad de Málaga / CBUA.This work is partially supported by the Autonomous Government ofAndalusia (Spain) under project UMA20-FEDERJA-108, project nameDetection, characterization and prognosis value of the non-obstructivecoronary disease with deep learning. It includes funds from the Euro-pean Regional Development Fund (ERDF). It is also partially supportedby the University of Málaga (Spain) under grants B1-2019_01, B1-2019_02 and B1-2021_20.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026