The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
Real time hand pose estimation using depth sensors
Abstract
This paper describes a depth image based real-time skeleton fitting algorithm for the hand, using an object recognition by parts approach, and the use of this hand modeler in an American Sign Language (ASL) digit recognition application. In particular, we created a realistic 3D hand model that represents the hand with 21 different parts. Random decision forests (RDF) are trained on synthetic depth images generated by animating the hand model, which are then used to perform per pixel classification and assign each pixel to a hand part. The classification results are fed into a local mode finding algorithm to estimate the joint locations for the hand skeleton. The system can process depth images retrieved from Kinect in real-time at 30 fps. As an application of the system, we also describe a support vector machine (SVM) based recognition module for the ten digits of ASL based on our method, which attains a recognition rate of 99.9% on live depth images in real-time1.
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Metadata
- Authors
- Keskin, C., Kirac, F., Kara, Y. E., & Akarun, L.
- Affiliations
- Boğaziçi University (TR)
- ORCID
- Akarun, Lale
- Venue
- 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 1228-1234
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2407277412
- Cited by
- 190
- References
- 16
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 8 · 2022: 2 · 2021: 6 · 2020: 10 · 2019: 14 · 2018: 18 · 2017: 12 · 2016: 18 · 2015: 28 · 2014: 26 · 2013: 22 · 2012: 14
- FWCI
- 15.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- hand pose estimation, depth sensors, 3D hand model, random forest, American Sign Language, real-time processing, Kinect sensor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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