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Thesis

Advancing human pose and gesture recognition

Abstract:

This thesis presents new methods in two closely related areas of computer vision: human pose estimation, and gesture recognition in videos.

In human pose estimation, we show that random forests can be used to estimate human pose in monocular videos. To this end, we propose a co-segmentation algorithm for segmenting humans out of videos, and an evaluator that predicts whether the estimated poses are correct or not. We further extend this pose estimator to new domains (with a transfer...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Research group:
Visual Geometry Group
Oxford college:
Wolfson College
Role:
Author

Contributors

Role:
Supervisor
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Funding agency for:
Pfister, T
Grant:
EP/I012001/1
Publication date:
2015
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
Language:
English
Keywords:
Subjects:
UUID:
uuid:64e5b1be-231e-49ed-b385-e87db6dbeed8
Local pid:
ora:11908
Deposit date:
2015-07-27

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