DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis

by   Juan Diego Ortega, et al.

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.


page 5

page 7


Attention Monitoring and Hazard Assessment with Bio-Sensing and Vision: Empirical Analysis Utilizing CNNs on the KITTI Dataset

Assessing the driver's attention and detecting various hazardous and non...

Robust Seatbelt Detection and Usage Recognition for Driver Monitoring Systems

Wearing a seatbelt appropriately while driving can reduce serious crash-...

Real-Time Driver Monitoring Systems through Modality and View Analysis

Driver distractions are known to be the dominant cause of road accidents...

Discovering and Explaining Driver Behaviour under HoS Regulations

World wide transport authorities are imposing complex Hours of Service r...

Face recognition for monitoring operator shift in railways

Train Pilot is a very tedious and stressful job. Pilots must be vigilant...

Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention

Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions ...

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-sensing and Vision-based Analysis, Evaluations and Insights

Automobiles for our roadways are increasingly utilizing advanced driver ...

Please sign up or login with your details

Forgot password? Click here to reset