NDLI: 3D driver pose estimation based on joint 2D

Content Provider	IET Digital Library
Author	Yao, Zhijie Liu, Yazhou Ji, Zexuan Sun, Quansen Lasang, Pongsak Shen, Shengmei
Abstract	Three-dimensional (3D) driver pose estimation is a promising and challenging problem for computer–human interaction. Recently convolutional neural networks have been introduced into 3D pose estimation, but these methods have the problem of slow running speed and are not suitable for driving scenario. In this study, the proposed method is based on two types of inputs, infrared image and point cloud obtained from time-of-flight camera. The authors propose a joint 2D–3D network incorporating image-based and point-based feature to promote the performance of 3D human pose estimation and run on a high speed. For point cloud with invalid points, the authors first do preprocess and then design a denoising module to handle this problem. Experiments on private driver data set and public Invariant-Top View data set show that the proposed method achieves efficient and competitive performance on 3D human pose estimation.
Starting Page	84
Ending Page	91
Page Count	8
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 3, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0089
Journal	IET Computer Vision
Publisher Date	2020-01-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	2D–3D Network Incorporating Image-based Camera Computer Vision And Image Processing Technique Computer–human Interaction Convolutional Neural Network Driver Information System Feature Extraction Image Recognition Infrared Image Joint 2D–3D Network Neural Computing Technique Point Cloud Pose Estimation Private Driver Data Three-dimensional Driver Pose Estimation Time-of-flight Camera Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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