Reference.
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We have presented DMD on this paper.
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Citation:
Ortega, J. D., Kose, N., Cañas, P., Chao, M.-A., Unnervik, A., Nieto, M., Otaegui, O., Salgado, L. (2020). DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis. In: A. Bartoli & A. Fusiello (eds), Computer Vision -- ECCV 2020 Workshops (pg. 387–405). Springer International Publishing.
Other publications regarding the DMD:
Cañas P.N., García M., Aranjuelo N., Nieto M., Iglesias A. and Rodríguez I. (2023) Dynamic Risk Assessment Methodology with an LDM-Based System for Parking Scenarios. IEEE 26thInternational Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023,pp. 5034-5039, doi: 10.1109/ITSC57777.2023.10422385
Urselmann, T., Cañas, P.N., Ortega, J.D., & Nieto, M. (2022). Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application. ECCV Workshops.
Ortega J., Cañas P., Nieto M. and Otaegui O, and Salgado L. (2022). Challenges of Large-Scale Multi-Camera Datasets for Driver Monitoring Systems. Sensors 22, no. 7: 2554. https://doi.org/10.3390/s22072554
Cañas P., Ortega J., Nieto M. and Otaegui O. (2021). Detection of Distraction-related Actions on DMD: An Image and a Video-based Approach Comparison.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 458-465. DOI: 10.5220/0010244504580465