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Author Sharp, Toby ♦ Cook, Mat ♦ Moore, Richard ♦ Shotton, Jamie ♦ Blake, Andrew ♦ Finocchio, Mark ♦ Fitzgibbon, Andrew ♦ Kipman, Alex
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract We propose a new method to quickly and accurately predict human pose---the 3D positions of body joints---from a single depth image, without depending on information from preceding frames. Our approach is strongly rooted in current object recognition strategies. By designing an intermediate representation in terms of body parts, the difficult pose estimation problem is transformed into a simpler per-pixel classification problem, for which efficient machine learning techniques exist. By using computer graphics to synthesize a very large dataset of training image pairs, one can train a classifier that estimates body part labels from test images invariant to pose, body shape, clothing, and other irrelevances. Finally, we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs in under 5ms on the Xbox 360. 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.
Description Affiliation: Microsoft Research, Cambridge, UK (Shotton, Jamie; Sharp, Toby; Fitzgibbon, Andrew; Blake, Andrew; Cook, Mat) || ST-Ericsson (Moore, Richard) || Xbox Incubation (Kipman, Alex; Finocchio, Mark)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 56
Issue Number 1
Page Count 9
Starting Page 116
Ending Page 124

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Source: ACM Digital Library