MSCT-Net：用于驾驶员疲劳检测的多流CNN-Transformer网络

Driver drowsiness is a major cause of fatal car accidents, and camera-based monitoring offers a promising way to detect it. Detecting drowsiness from video requires modeling both facial appearance and its temporal evolution. In this article, we propose MSCT-Net, a multi-stream CNN–Transformer network for this purpose. Region-specific CNN backbones encode the face, right eye and mouth in every frame. Then, a single Transformer encoder jointly models their temporal dynamics across regions and time. MSCT-Net is trained from scratch on the challenging NTHU Drowsy Driver Detection dataset, which co