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Real-time human pose recognition in parts from single depth images (2011)
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| Venue: | IN CVPR |
| Citations: | 568 - 17 self |
BibTeX
@INPROCEEDINGS{Shotton11real-timehuman,
author = {Jamie Shotton and Andrew Fitzgibbon and Mat Cook and Toby Sharp and Mark Finocchio and Richard Moore and Alex Kipman and Andrew Blake},
title = {Real-time human pose recognition in parts from single depth images},
booktitle = { IN CVPR },
year = {2011},
publisher = {}
}
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Abstract
We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.
Keyphrases
single depth image real-time human pose recognition varied training dataset local mode intermediate body part representation new method related work body shape body joint body part classification result consumer hardware difficult pose estimation problem temporal information several body joint high accuracy simpler per-pixel classification problem real test set art accuracy object recognition approach
