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

Author:

Yang Cong1ORCID,Yang Zhenyu2,Li Weiyu2,See John3ORCID

Affiliation:

1. School of Future Science and Engineering, Soochow University, Suzhou, China

2. Smart Auto Group, Horizon Robotics, Nanjing, China

3. School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia Campus, Putrajaya, Malaysia

Funder

Natural Science Foundation of the Jiangsu Higher Education Institutions of China

Halo Special Research Fund from Horizon Robotics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Advanced detection techniques for driver drowsiness: a comprehensive review of machine learning, deep learning, and physiological approaches;Multimedia Tools and Applications;2024-07-05

2. Robust Eye Blink Detection Using Dual Embedding Video Vision Transformer;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

3. Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study;2023 28th International Conference on Automation and Computing (ICAC);2023-08-30

4. Drowsiness Detection for Drivers using IoT;2023 7th International Conference on Computing Methodologies and Communication (ICCMC);2023-02-23

5. 4D: A Real-Time Driver Drowsiness Detector Using Deep Learning;Electronics;2023-01-03

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