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MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset.

, , , and . CAIP (1), volume 11678 of Lecture Notes in Computer Science, page 518-529. Springer, (2019)

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Vision-based human action recognition: An overview and real world challenges., , , and . Digit. Investig., (2020)Safe Driving : Driver Action Recognition using SURF Keypoints., , , and . ICM, page 60-63. IEEE, (2018)Deep learning-based hard spatial attention for driver in-vehicle action monitoring., , , and . Expert Syst. Appl., (June 2023)GF2PReID: A Novel Framework for Person Re-IDentification Using Generative Networks., , , and . CW, page 102-109. IEEE, (2023)Hard Spatial Attention Framework for Driver Action Recognition at Nighttime., , , and . ICAART (3), page 964-971. SCITEPRESS, (2023)Hand-Crafted Features vs Deep Learning for Pedestrian Detection in Moving Camera., , , , and . Traitement du Signal, 37 (2): 209-216 (2020)MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset., , , and . CAIP (1), volume 11678 of Lecture Notes in Computer Science, page 518-529. Springer, (2019)Deep CNN-based Pedestrian Detection for Intelligent Infrastructure., , , , and . ATSIP, page 1-6. IEEE, (2020)A Multi-Convolutional Stream for Hybrid network for Driver Action Recognition at Nighttime., , , and . CoDIT, page 337-342. IEEE, (2022)Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores Using Graph Neural Networks and Meta-learning., and . PRIME@MICCAI, volume 13564 of Lecture Notes in Computer Science, page 203-211. Springer, (2022)