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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,523
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A. W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995316 · Cited by 3,545
Vision-based hand pose estimation: A review
Erol, A., Bebis, G. N., Nicolescu, M., Boyle, R. D., & Twombly, X. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.012 · Cited by 866
Estimating 3D hand pose from a cluttered image
Athitsos, V., & Sclaroff, S. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211500 · Cited by 387
Model-Based 3D Hand Pose Estimation from Monocular Video
Gorce, M. D. L., Fleet, D. J., & Nikos Paragios, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.33 · Cited by 271
Model-based 3D tracking of an articulated hand
Stenger, B., Mendonça, P. R. S., & Cipolla, R. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2001.990976 · Cited by 266
Hand gesture recognition using a neural network shape fitting technique
Stergiopoulou, E., & Papamarkos, N. (2009) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2009.03.008 · Cited by 264
Hand gesture recognition using depth data
Liu, X., & Fujimura, K. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301587 · Cited by 205
3D hand pose reconstruction using specialized mappings
Rosales, R., Athitsos, V., Sigal, L., & Sclaroff, S. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937543 · Cited by 190
Real-time Hand Pose Recognition Using Low-Resolution Depth Images
Mo, Z., & Neumann, U. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.237 · Cited by 113
Dynamic Hand Pose Recognition Using Depth Data
Suryanarayan, P., Subramanian, A., & Mandalapu, D. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.760 · Cited by 106
Real-time hand posture recognition using range data
Malassiotis, S., & Strintzis, M. G. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2007.11.007 · Cited by 84
Regression-based Hand Pose Estimation from Multiple Cameras
de Campos, T. E., & Murray, D. W. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.252 · Cited by 75
Research works printed but not linked: 4
- welch, 1995, An introduction to the Kalman filter
- tipping, 2001, Sparse Bayesian Learning and the Relevance Vector Machine
- oikonomidis, 2011, Markerless and efficient 26-DOF hand pose recovery, Proceedings of the 10th Asian Conference on Computer Vision-Volume Part, 3, 744
- redmond, 0, Kinect for Xbox, 360
Cited by
Sorted by citations
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
Han, J., Shao, L., Xu, D., & Shotton, J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2265378 · Cited by 1,360
GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB
Mueller, F., Bernard, F., Sotnychenko, O., Mehta, D., Sridhar, S., Casas, D., & Theobalt, C. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00013 · Cited by 516
Ohn-Bar, E., & Trivedi, M. M. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2337331 · Cited by 454
ModDrop: Adaptive Multi-Modal Gesture Recognition
Neverova, N., Wolf, C., Taylor, G., & Nebout, F. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2461544 · Cited by 420
Hand gesture recognition with depth images: A review
Suarez, J., & Murphy, R. R. (2012) · IEEE RO-MAN · doi:10.1109/roman.2012.6343787 · Cited by 394
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 ›
Are you an author, or do you see a problem with this page? Request a review
