Depth-Based Hand Pose Estimation: Methods, Data, and Challenges
- Publisher:
- SPRINGER
- Publication Type:
- Journal Article
- Citation:
- International Journal of Computer Vision, 2018, 126, (11), pp. 1180-1198
- Issue Date:
- 2018-11-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| s11263-018-1081-7.pdf | Published version | 4.13 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.
Please use this identifier to cite or link to this item: