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Upper body pose estimation with temporal sequential forests

Abstract:

Our objective is to efficiently and accurately estimate human upper body pose in gesture videos. To this end, we build on the recent successful applications of random forests (RF) classifiers and regressors, and develop a pose estimation model with the following novelties: (i) the joints are estimated sequentially, taking account of the human kinematic chain. This means that we don’t have to make the simplifying assumption of most previous RF methods – that the joints are estimated independen...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
Brasenose College
Role:
Author
EP/I01229X/1/Engineering and Physical Sciences Research Council More from this funder
EP/I012001/1/Engineering and Physical Sciences Research Council More from this funder
Publisher:
British Machine Vision Association Publisher's website
Host title:
BMVC 2014 - Proceedings of the British Machine Vision Conference 2014
Journal:
BMVC 2014 - Proceedings of the British Machine Vision Conference 2014 Journal website
Pages:
1-12
Publication date:
2014-01-01
Event location:
Nottingham
Source identifiers:
678983
Keywords:
Pubs id:
pubs:678983
UUID:
uuid:aeb66645-27ee-4b1f-a765-ce9f1b68142a
Local pid:
pubs:678983
Deposit date:
2017-02-09

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