DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
This addresses the need for better datasets to advance driver monitoring, crucial for automated driving, but is incremental as it builds on existing data collection efforts.
The authors tackled the bottleneck of lacking large datasets for Driver Monitoring Systems by introducing DMD, a comprehensive multi-modal dataset with 41 hours of video from 37 drivers, and demonstrated its utility through a real-time behavior recognition system that achieved enhanced accuracy.
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to SAE Level-3. In this paper, we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which includes real and simulated driving scenarios: distraction, gaze allocation, drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A comparison with existing similar datasets is included, which shows the DMD is more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated by extracting a subset of it, the dBehaviourMD dataset, containing 13 distraction activities, prepared to be used in DL training processes. Furthermore, we propose a robust and real-time driver behaviour recognition system targeting a real-world application that can run on cost-efficient CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated with different types of fusion strategies, which all reach enhanced accuracy still providing real-time response.