@inproceedings{ed1388eb0b964b4c89416ab9d38e220e,
title = "YOLO Deep-Learning Based Driver Behaviors Detection and Effective Gaze Estimation by Head Poses for Driver Monitor System",
abstract = "This work develops a non-contact driver behavior monitoring system based on intelligent visual sensing to improve the driving safety. By the deep learning technology with YOLO, a car-specification near-infrared (NIR) camera is installed to detect the driver's behaviors and gaze directions. The YOLO-based head pose inference method is developed, and the driver's gaze directions are predicted with the simplified calibration. In experiments, the input size of YOLOv4-tiny based model is set to 416x416 pixels. After functional tests, the proposed method performs average precision (AP) to be 86.58% for detecting eleven classes including driver's objects and behaviors. Besides, the proposed gaze estimation technology by driver's head poses performs average detection accuracy up to 83% to estimate twelve driver's gaze directions.",
keywords = "driver behaviors detection, Driver monitor system (DMS), gaze estimation, head poses, YOLOv4-tiny",
author = "Fang, {Yi Chiao} and Zhao, {Xi Liang} and Lin, {Hsuan Yu} and Yang, {Yu Cheng} and Guo, {Jiun In} and Fan, {Chih Peng}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/GCCE59613.2023.10315275",
language = "English",
series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "82--83",
booktitle = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
address = "United States",
}