Benchmarking 2D Egocentric Hand Pose Datasets
Hand pose estimation from egocentric video is a topic of significant interest with broad implications for human-computer interactions, assistive technologies, activity recognition, and robotics. The efficacy of modern machine learning models depends on the quality of data used for their training. Th...
Saved in:
| Main Authors: | Olga Taran, Damian M. Manzone, Jose Zariffa |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11015740/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Detecting Activities of Daily Living in Egocentric Video to Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings
by: Adesh Kadambi, et al.
Published: (2025-01-01) -
RehabHand—A New Physical Rehabilitation Training Dataset: Construction and Benchmark Performances of the Relevant Hand Tasks
by: Sinh Huy Nguyen, et al.
Published: (2025-01-01) -
LightHand99K: A Synthetic Dataset for Hand Pose Estimation With Wrist-Worn Cameras
by: Jeongho Lee, et al.
Published: (2025-01-01) -
A Survey of the State of the Art in Monocular 3D Human Pose Estimation: Methods, Benchmarks, and Challenges
by: Yan Guo, et al.
Published: (2025-04-01) -
Cross-View Correspondence Modeling for Joint Representation Learning Between Egocentric and Exocentric Videos
by: Zhehao Zhu, et al.
Published: (2025-01-01)