RT Conference Proceedings
T1 Anomalous trajectory detection for automated traffic video surveillance
A1 Fernández-Rodríguez, Jose David
A1 García-González, Jorge
A1 Benítez-Rochel, Rafaela
A1 Molina-Cabello, Miguel Ángel
A1 López-Rubio, Ezequiel
A1 García-González, Jorge
K1 Diseño orientado a objetos
K1 Videovigilancia electrónica
K1 Visión artificial (Robótica)
AB Vehicle trajectories extracted from traffic video sequences can be helpful for many purposes. In particular, the analysis of detected anomalous trajectories may enhance drivers’ safety. This work proposes a methodology to detect anomalous vehicle trajectories by using a vehicle detection, a vehicle tracking and a processing of the tracking information steps. Once trajectories are detected, their velocity vectors are estimated and an anomaly value is computed for each trajectory by comparing its vector with those from its nearest neighbours. The management of these anomaly values allows considering which trajectories are  suitable to be potentially anomalous considered. Real and synthetic videos have been included in the experiments to perform the goodness of the proposal.
YR 2022
FD 2022
LK https://hdl.handle.net/10630/24353
UL https://hdl.handle.net/10630/24353
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía TechGobierno de España bajo el proyecto RTI2018-094645-B-I00Junta de Andalucía bajo el proyecto UMA18-FEDERJA-084
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026