BROWSE
Detail
Conference Paper
3D Human Pose Estimation Using Egocentric Depth Data
Cited 0 time in 
Share 
Share
- Authors
- Seongmin Baek, Youn-Hee Gil, Yejin Kim
- Issue Date
- 2024-10
- Citation
- Symposium on Virtual Reality Software and Technology (VRST) 2024, pp.1-2
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we present a novel approach for 3D human pose estimation using depth data from egocentric viewpoints. Depth data has the advantage that it is less sensitive to color and lighting changes. We acquired depth data streamed from multiple depth cameras attached to a user’s head and calibrated them into a depth map. For joint detection, a ResNet-based network was optimized with the skeletal joints of a Kinect camera. Unlike previous approaches, the proposed approach can track 3D human poses in an egocentric setup with a small dataset.
- KSP Keywords
- 3D human pose estimation, Depth Data, Depth Map, Depth camera, Joint detection, Novel approach, Skeletal joints, Small dataset, kinect camera