# A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

arXiv: https://arxiv.org/abs/1904.07312 · HTML: https://mlsanity.com/paper/1904.07312/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 19 of 19)

- [Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving](https://mlsanity.com/paper/2410.21086/) (2024)
- [Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework](https://mlsanity.com/paper/2209.01401/) (2022)
- [A Survey on Drowsiness Detection -- Modern Applications and Methods](https://mlsanity.com/paper/2408.12990/) (2024)
- [Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation](https://mlsanity.com/paper/2101.08621/) (2021)
- [Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video](https://mlsanity.com/paper/2303.16053/) (2023)
- [POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search](https://mlsanity.com/paper/2410.09583/) (2024)
- [CogPortrait: Fine-Grained Eye-Region Control in Portrait Animation via Hierarchical Agent Planning](https://mlsanity.com/paper/2605.28056/) (2026)
- [Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDD](https://mlsanity.com/paper/2112.10298/) (2021)
- [Multi-Attention Fusion Drowsy Driving Detection Model](https://mlsanity.com/paper/2312.17052/) (2023)
- [UL-DD: A Multimodal Drowsiness Dataset Using Video, Biometric Signals, and Behavioral Data](https://mlsanity.com/paper/2507.13403/) (2025)
- [FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework](https://mlsanity.com/paper/2104.12086/) (2021)
- [How Facial Features Convey Attention in Stationary Environments](https://mlsanity.com/paper/2111.14931/) (2021)
- [In-vehicle alertness monitoring for older adults](https://mlsanity.com/paper/2208.08091/) (2022)
- [Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers](https://mlsanity.com/paper/2209.15148/) (2022)
- [Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol](https://mlsanity.com/paper/2309.08021/) (2023)
- [Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learning](https://mlsanity.com/paper/2508.00287/) (2025)
- [Vision-Based Driver Drowsiness Monitoring: Comparative Analysis of YOLOv5-v11 Models](https://mlsanity.com/paper/2509.17498/) (2025)
- [Blinking Beyond EAR: A Stable Eyelid Angle Metric for Driver Drowsiness Detection and Data Augmentation](https://mlsanity.com/paper/2511.19519/) (2025)
- [HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment](https://mlsanity.com/paper/2604.08435/) (2026)

## Abstract (arXiv)

Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-to-deploy real-world systems to detect the onset of drowsiness.In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.