RT Journal Article
T1 Unsupervised detection of inflows and outflows in intersection traffic videos
A1 Jiménez-Partinen, Ariadna
A1 Fernández-Rodríguez, José David
A1 Benito-Picazo, Jesús
A1 Molina-Cabello, Miguel Ángel
A1 Benítez-Rochel, Rafaela
A1 López-Rubio, Ezequiel
K1 Videovigilancia electrónica - Tráfico
K1 Diseño orientado a objetos
AB As traffic cameras become prevalent, the automatic analysis of traffic scenes presents new opportunities and challenges. Advances in deep learning allow for automated characterization of traffic in such videos. This work aims to understand traffic flow without human supervision, focusing on the localization of road intersections. For this purpose, a three-stage method is proposed that uses a deep neural network for vehicle detection, an object tracker to recover vehicle trajectories, and unsupervised machine learning to detect potential incoming and outgoing traffic flows. The approach has been tested on a variety of real and synthetic videos, with satisfactory results across different camera positions, traffic patterns, and weather conditions. As a key part of the methodology, five options for clustering starting and ending track points were tested. These options included a basic strategy based on predefined spatially localized clusters, and the K-means algorithm with two methods to determine the optimal number of clusters: the Elbow method and the Silhouette score. Additionally, Mean Shift and the Density-Based Spatial Clustering of Applications with Noise were evaluated. An exhaustive analysis of the proposed clustering methods was conducted, including runtime at each stage, performance metrics, and the addition of noise to simulate tracker failures. The results demonstrated the feasibility of the proposed methodology and concluded that Mean Shift is the most suitable clustering method due to its balance of high performance, low runtime, and stable behavior against abnormal trajectory points.
PB Sage
YR 2026
FD 2026
LK https://hdl.handle.net/10630/46585
UL https://hdl.handle.net/10630/46585
LA eng
NO Jiménez-Partinen, A., Fernández-Rodríguez, J. D., Benito-Picazo, J., Molina-Cabello, M. A., Benítez-Rochel, R., & López-Rubio, E. (2026). Unsupervised detection of inflows and outflows in intersection traffic videos. Integrated Computer-Aided Engineering. https://doi.org/10.1177/10692509261442319
NO Política de acceso abierto: https://openpolicyfinder.jisc.ac.uk/publication/1823
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 may 2026