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

• URL: http://arxiv.org/abs/2008.12085v1
• Date: Thu, 27 Aug 2020 12:33:54 GMT
• Title: DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
• Authors: Juan Diego Ortega, Neslihan Kose, Paola Ca\~nas, Min-An Chao, Alexander Unnervik, Marcos Nieto, Oihana Otaegui, Luis Salgado
• Abstract summary: Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS) The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development. In this paper, we introduce the Driver Monitoring dataset (DMD), an extensive dataset which includes real and simulated driving scenarios.
• Score: 54.198237164152786
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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