100-Driver: A Large-scale, Diverse Dataset for Distracted Driver Classification

Abstract

Distracted driver classification (DDC) plays an important role in ensuring driving safety. Although many datasets are introduced to support the study of DDC, most of them are small in data size and are short of diversity in environmental variations. This largely limits the development of DDC since many practical problems such as the cross-modality setting cannot be fully studied. In this paper, we introduce 100-Driver, a large-scale, diverse posture-based distracted diver dataset, with more than 470K images taken by 4 cameras observing 100 drivers over 79 hours from 5 vehicles. 100-Driver involves different types of variations that closely meet the real-world applications, including changes in the vehicle, person, camera view, lighting and modality. We provide a detailed analysis of 100-Driver and present 4 settings for investigating practical problems of DDC, including the traditional setting without domain shift and 3 challenging settings (i.e., cross-modality, cross-view, and cross-vehicle) with domain shifts. We conduct comprehensive experiments on these 4 settings with state-the-of-art techniques and show several insights to the future study of DDC. Our 100-Driver will be publicly available offering new opportunities to advance the development of DDC.