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Ego+X: an egocentric vision system for global 3D human pose estimation and social interaction characterization
File | Description | Size | Format | |
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IROS22_0868_FI.pdf | Accepted version | 4.46 MB | Adobe PDF | View/Open |
Title: | Ego+X: an egocentric vision system for global 3D human pose estimation and social interaction characterization |
Authors: | Liu, Y Yang, J Gu, X Guo, Y Yang, G-Z |
Item Type: | Conference Paper |
Abstract: | Egocentric vision is an emerging topic, which has demonstrated great potential in assistive healthcare scenarios, ranging from human-centric behavior analysis to personal social assistance. Within this field, due to the heterogeneity of visual perception from first-person views, egocentric pose estimation is one of the most significant prerequisites for enabling various downstream applications. However, existing methods for egocentric pose estimation mainly focus on predicting the pose represented in the camera coordinates from a single image, which ignores the latent cues in the temporal domain and results in less accuracy. In this paper, we propose Ego+X, an egocentric vision based system for 3D canonical pose estimation and human-centric social interaction characterization. Our system is composed of two head-mounted egocentric cameras, where one is faced downwards and the other looks outwards. By leveraging the global context provided by visual SLAM, we first propose Ego-Glo for spatial-accurate and temporal-consistent egocentric 3D pose estimation in the canonical coordinate system. With the help of an egocentric camera looking outwards, we then propose Ego-Soc by extending Ego-Glo to various social interaction tasks, e.g., object detection and human-human interaction. Quantitative and qualitative experiments have been conducted to demonstrate the effectiveness of our proposed Ego+X. |
Issue Date: | 26-Dec-2022 |
Date of Acceptance: | 30-Jun-2022 |
URI: | http://hdl.handle.net/10044/1/98397 |
DOI: | 10.1109/IROS47612.2022.9981710 |
Publisher: | https://ieeexplore.ieee.org/document/9981710 |
Start Page: | 5271 |
End Page: | 5277 |
Copyright Statement: | Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Conference Name: | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Publication Status: | Published |
Start Date: | 2022-10-23 |
Finish Date: | 2022-10-27 |
Conference Place: | Kyoto |
Online Publication Date: | 2022-12-26 |
Appears in Collections: | Central Faculty |