计算机科学
地标
人工智能
计算机视觉
钥匙(锁)
特征(语言学)
面部表情
特征提取
面子(社会学概念)
模式识别(心理学)
社会科学
哲学
语言学
计算机安全
社会学
作者
Lie Yang,Haohan Yang,Henglai Wei,Zhongxu Hu,Chen Lv
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-13
标识
DOI:10.1109/tits.2023.3346054
摘要
Driver drowsiness detection is of great significance in improving driving safety and has been widely studied in recent years. However, some existing methods have not fully utilized the drowsiness-related information, and some methods are susceptible to interference from the redundant information of input data. To address these issues, a video-based driver drowsiness detection method according to the key facial features including facial landmarks and local facial areas (VBFLLFA) is proposed in this paper. In order to fully utilize the key facial features related to drowsiness and exclude the interference of redundant information, the head movement information is obtained through facial landmark analysis and the movement information of eyes and mouth is acquired from the local facial areas. And the spatial filtering based on the common spatial pattern (CSP) algorithm is introduced to improve the discrimination of different classes of samples. To adequately extract the temporal and spatial features, a two-branch multi-head attention (TB-MHA) module is designed in this paper. Furthermore, the center loss with center vector distance penalty is introduced to further improve the discrimination of different classes of samples in the feature space. In addition to two public datasets, we specifically create a novel video-based driver drowsiness detection (VBDDD) dataset to evaluate the effectiveness of our method. The experimental results verify that our method can achieve very excellent performance in driver drowsiness detection tasks.
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