Unsupervised Detection of Incoming and Outgoing Traffic Flows in Video Sequences. - Fernández-Rodríguez, Jose David; Carmona-Martínez, Pablo; Benítez-Rochel, Rafaela; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (Springer, 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.
Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policies
Keywords: Diseño orientado a objetos; Videovigilancia electrónica - Tráfico; Unsupervised learning; Object tracking; Object detection; Video surveillance; Deep learning