2026-06-01T20:28:21Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/322582026-02-03T12:18:59Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Fernández-Rodríguez, Jose David author Carmona-Martínez, Pablo author Benítez-Rochel, Rafaela author Molina-Cabello, Miguel Ángel author López-Rubio, Ezequiel author 2024 As traffic cameras become prevalent, and a considerable amount of traffic videos are stored for various purposes, new possibilities and challenges open in the automatic analysis of traffic scenes. Advances in deep learning also enable new ways to characterize traffic in such videos automatically. This work is motivated by the need to understand traffic flow without human supervision, especially the localization of road intersections in scenes from traffic cameras. For this purpose, a method is proposed that uses a deep learning neural network for vehicle detection, an object tracker to recover vehicle trajectories from the detections, and unsupervised machine learning techniques to detect potential incoming and outgoing traffic flows from the vehicle trajectories in the video sequences. A wide range of real and synthetic videos have been used to test the goodness of the proposal with satisfactory results, from traffic cameras at different heights and angles, different traffic patterns, and various weather conditions. Fernández-Rodríguez, J.D., Carmona-Martínez, P., Benítez-Rochel, R., Molina-Cabello, M.A., López-Rubio, E. (2024). Unsupervised Detection of Incoming and Outgoing Traffic Flows in Video Sequences. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_1 https://hdl.handle.net/10630/32258 10.1007/978-3-031-61137-7_1 Diseño orientado a objetos Videovigilancia electrónica - Tráfico Unsupervised Detection of Incoming and Outgoing Traffic Flows in Video Sequences.