2026-06-03T05:27:28Zhttps://riuma.uma.es/rest/oai/request

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

Automated detection of vehicles with anomalous trajectories in traffic surveillance videos. Fernández-Rodríguez, Jose David García-González, Jorge Benítez-Rochel, Rafaela Molina-Cabello, Miguel Ángel Ramos-Jiménez, Gonzalo Pascual López-Rubio, Ezequiel Videovigilancia electrónica Visión artificial (Robótica) Demodulación (Electrónica) 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. 2024-02-09T07:04:15Z 2024-02-09T07:04:15Z 2024-02-09T07:04:15Z 2023-05-10 journal article 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 eng open access IOS Press