FatigueView: A Multi-Camera Video Dataset for Vision-Based Drowsiness Detection

Abstract

Although vision-based drowsiness detection approaches have achieved great success on empirically organized datasets, it remains far from being satisfactory for deployment in practice. One crucial issue lies in the scarcity and lack of datasets that represent the actual challenges in real-world applications, e.g. tremendous variation and aggregation of visual signs, challenges brought on by different camera positions and camera types. To promote research in this field, we introduce a new large-scale dataset, FatigueView, that is collected by both RGB and infrared (IR) cameras from five different positions. It contains real sleepy driving videos and various visual signs of drowsiness from subtle to obvious, e.g. with 17,403 different yawning sets totaling more than 124 million frames, far more than recent actively used datasets. We also provide hierarchical annotations for each video, ranging from spatial face landmarks and visual signs to temporal drowsiness locations and levels to meet different research requirements. We structurally evaluate representative methods to build viable baselines. With FatigueView, we would like to encourage the community to adapt computer vision models to address practical real-world concerns, particularly the challenges posed by this dataset.