Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning

ArXi:2411.13181v3 Announce Type: replace-cross The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies nstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under conditions that differ from the