Cargando…
An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling
Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, gaze, blinking, and yawning. To solve the challenge of head pose and gaze es...
Autores principales: | Hu, Zhongxu, Zhang, Yiran, Xing, Yang, Li, Qinghua, Lv, Chen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573517/ https://www.ncbi.nlm.nih.gov/pubmed/36236513 http://dx.doi.org/10.3390/s22197415 |
Ejemplares similares
-
Human–Machine Telecollaboration Accelerates the Safe Deployment of Large-Scale Autonomous Robots During the COVID-19 Pandemic
por: Hu, Zhongxu, et al.
Publicado: (2022) -
A Novel Decoupled Feature Pyramid Networks for Multi-Target Ship Detection
por: Xue, Wentao, et al.
Publicado: (2023) -
Digital signaling decouples activation probability and population heterogeneity
por: Kellogg, Ryan A, et al.
Publicado: (2015) -
Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism
por: Peng, Wei, et al.
Publicado: (2023) -
A Universal Decoupled Training Framework for Human Parsing
por: Li, Yang, et al.
Publicado: (2022)