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Cited by 576

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Efficient Human Pose Estimation from Single Depth Images

Shotton, Jamie, Girshick, Ross, Fitzgibbon, Andrew, Sharp, Toby, Cook, Mat, Finocchio, Mark, Moore, Richard, Kohli, Pushmeet, Criminisi, Antonio, Kipman, Alex, Blake, Andrew·2013·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2012.241
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Abstract

We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.

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Cites

Research works printed but not linked: 56

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Metadata

Authors
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., & Blake, A.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-12
Volume
35
Issue
12
Pages
2821-2840
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2012.241
OpenAlex
W2036196300
Cited by
576
References
6
FWCI
43.93
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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