Adaptive Frame-Aware Network for Driver Monitoring Systems

Driver monitoring systems (DMS) are crucial for enhancing road safety by detecting driver behaviors and states such as drowsiness, distraction, and other potentially hazardous actions. In this research, we propose a novel approach using Adaptive Frame Aware Network (AFAN) for sequence classification in driver monitoring. Our model leverages frame embedding, adaptive attention, and sequence classification to accurately classify driver behaviors. We evaluate our model on two distinct datasets, including sequences collected on a private test setup, demonstrating its effectiveness in diverse conditions. The results show significant improvements over existing methods, achieving up to more than twice faster inference time and up to 88% memory size reduction, highlighting the potential of AFAN for real-time driver monitoring applications.