DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis
The importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompan...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10385044/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849340617071525888 |
|---|---|
| author | Jiho Park Junghye Kim Yujung Gil Dongho Kim |
| author_facet | Jiho Park Junghye Kim Yujung Gil Dongho Kim |
| author_sort | Jiho Park |
| collection | DOAJ |
| description | The importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompanied by annotation data, including motion capture, RGB video, image, and 3D object modeling data. It features 63 action classes involving interactions with 60 common furniture and electronic devices. Each action class comprises approximately 1,000 motion capture data representing 3D skeleton data and corresponding RGB video and 3D object modeling data, resulting in 67,505 motion capture data samples. It offers comprehensive 3D structural information of the human, RGB images and videos, and point cloud data for 60 objects, collected through the participation of 126 subjects to ensure inclusivity and account for diverse human body types. To validate our dataset, we leveraged MMNet, a 3D human action recognition model, achieving Top-1 accuracy of 91.51% and 92.29% using the skeleton joint and bone methods, respectively. Beyond human action recognition, our versatile dataset is valuable for various 3D human action analysis research endeavors. |
| format | Article |
| id | doaj-art-a154e99be1ea42d99841c7b7ffaf3c78 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a154e99be1ea42d99841c7b7ffaf3c782025-08-20T03:43:52ZengIEEEIEEE Access2169-35362024-01-01128780879010.1109/ACCESS.2024.335188810385044DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action AnalysisJiho Park0https://orcid.org/0000-0002-1048-3881Junghye Kim1https://orcid.org/0009-0005-3608-7616Yujung Gil2https://orcid.org/0000-0002-6139-9831Dongho Kim3https://orcid.org/0000-0003-3349-103XDepartment of Artificial Intelligence, Dongguk University, Seoul, South KoreaDepartment of Information and Communication Engineering, Dongguk University, Seoul, South KoreaDepartment of Computer Science and Engineering, Dongguk University, Seoul, South KoreaSoftware Education Institute, Dongguk University, Seoul, South KoreaThe importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompanied by annotation data, including motion capture, RGB video, image, and 3D object modeling data. It features 63 action classes involving interactions with 60 common furniture and electronic devices. Each action class comprises approximately 1,000 motion capture data representing 3D skeleton data and corresponding RGB video and 3D object modeling data, resulting in 67,505 motion capture data samples. It offers comprehensive 3D structural information of the human, RGB images and videos, and point cloud data for 60 objects, collected through the participation of 126 subjects to ensure inclusivity and account for diverse human body types. To validate our dataset, we leveraged MMNet, a 3D human action recognition model, achieving Top-1 accuracy of 91.51% and 92.29% using the skeleton joint and bone methods, respectively. Beyond human action recognition, our versatile dataset is valuable for various 3D human action analysis research endeavors.https://ieeexplore.ieee.org/document/10385044/3D human action analysishuman action recognitionhuman activity understandingmotion capturemulti-modal dataset |
| spellingShingle | Jiho Park Junghye Kim Yujung Gil Dongho Kim DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis IEEE Access 3D human action analysis human action recognition human activity understanding motion capture multi-modal dataset |
| title | DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis |
| title_full | DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis |
| title_fullStr | DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis |
| title_full_unstemmed | DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis |
| title_short | DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis |
| title_sort | dgu hao a dataset with daily life objects for comprehensive 3d human action analysis |
| topic | 3D human action analysis human action recognition human activity understanding motion capture multi-modal dataset |
| url | https://ieeexplore.ieee.org/document/10385044/ |
| work_keys_str_mv | AT jihopark dguhaoadatasetwithdailylifeobjectsforcomprehensive3dhumanactionanalysis AT junghyekim dguhaoadatasetwithdailylifeobjectsforcomprehensive3dhumanactionanalysis AT yujunggil dguhaoadatasetwithdailylifeobjectsforcomprehensive3dhumanactionanalysis AT donghokim dguhaoadatasetwithdailylifeobjectsforcomprehensive3dhumanactionanalysis |