2026-05-30T09:37:15Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/302222026-02-03T11:23:52Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Fernández-Rodríguez, Jose David author García-González, Jorge author Benítez-Rochel, Rafaela author Molina-Cabello, Miguel Ángel author Ramos-Jiménez, Gonzalo Pascual author López-Rubio, Ezequiel author 2023-05-10 Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to automatically address that by using video sequences from traffic cameras. The proposal detects vehicles frame by frame, tracks their trajectories across frames, estimates velocity vectors, and compares them to velocity vectors from other spatially adjacent trajectories. From the comparison of velocity vectors, trajectories that are very different (anomalous) from neighboring trajectories can be detected. In practical terms, this strategy can detect vehicles in wrong-way trajectories. Some components of the model are off-the-shelf, such as the detection provided by recent deep learning approaches; however, several different options are considered and analyzed for vehicle tracking. The performance of the system has been tested with a wide range of real and synthetic traffic videos. Fernández-Rodríguez, J. D., García-González, J., Benítez-Rochel, R., Molina-Cabello, M. A., Ramos-Jiménez, G., & López-Rubio, E. (2023). Automated detection of vehicles with anomalous trajectories in traffic surveillance videos. Integrated Computer-Aided Engineering, 30(3), 293–309. https://hdl.handle.net/10630/30222 10.3233/ICA-230706 Videovigilancia electrónica Visión artificial (Robótica) Demodulación (Electrónica) Automated detection of vehicles with anomalous trajectories in traffic surveillance videos.