.png){kind=icon}
返回
Driver Pose Estimation Using Recurrent Lightweight Network and Virtual Data Augmented Transfer Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2019.2921325.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Driver poses recognition contains three tasks such as body joint, head angle, and face landmark estimation, which is of paramount interest for the advanced driver assistance systems (ADAS). Recently proposed methods intend to use deeper and more complicated networks to achieve better performance, which leads to heavy models that are not feasible for the resource limited applications such as ADAS. To resolve this issue, we have worked on the following aspects: 1) a lightweight network model, which is referred to as recurrent multi-task thin net (RM-ThinNet), has been proposed which was especially designed for the computationally and memory limited devices; 2) a recurrent structure has been introduced to handle the scale difference and dependency between different tasks, and this recurrence ensures the different tasks are accomplished at different stages and their outputs can augment each other; and 3) a virtual data synthesization pipeline and a couple transfer learning method have been presented, by which network can be learnt effectively by relatively a small number of real data. Comparisons with the state-of-the-art methods reveal the superiority of the proposed method and competitive performance can be achieved with smaller model size and faster speed.
Keyword:
Driver joints detection
head angle estimation
face landmark localization
transfer learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.4
论文数:
9.5K
被引数:
6.3W
机构
暂无机构信息
引用论文
Design and Implementation of Energy Management System Based on Spring Boot Framework.png)
Information
OAAI
InformationIF0
OAAI分享收藏Molecular analysis suggests oligoclonality and metastasis of endometriosis lesions across anatomically defined subtypes分子分析表明，子宫内膜异位症病变的寡克隆性和转移跨越解剖学定义的亚型
OAAI
OAAI分享收藏
查看更多内容