文章基本信息

标题：ドライバ姿勢と動作のマルチタスク学習による高速かつ高精度なドライバ動作認識
本地全文：下载
作者：西行健太 ; 日向匡史 ; 田崎博等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2021
卷号：36
期号：2
页码：1-10
DOI：10.1527/tjsai.36-2_A-K93
出版社：The Japanese Society for Artificial Intelligence
摘要：Driver Action Recognition is a key component in driver monitoring systems, which is helpful for the safety management of commercial vehicles. Compared with traditional human action recognition tasks, driver action recognition is required to be fast and accurate on embedded systems. We propose a fast and accurate driver action recognition method that is composed of CNN based driver pose estimation and RNN based driver action recognition. We train our network model with multi-task learning includes localizing and detecting each body part of the driver, classifying state of each body part, and recognizing driver action at once. Our multi-task learning for the proposed model achieves a significant improvement compared to state-of-the-art human action recognition methods with limited computational resources. We also perform ablation study of our methods which composed of the driver pose localization, detection, and classification.
关键词：driver monitoring;action recognition;multi-task learning;pose estimation