Thesis
Advancing human pose and gesture recognition
- Abstract:
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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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Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Pfister, T
Grant:
EP/I012001/1
Bibliographic Details
- Publication date:
- 2015
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- Oxford University, UK
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
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uuid:64e5b1be-231e-49ed-b385-e87db6dbeed8
- Local pid:
- ora:11908
- Deposit date:
- 2015-07-27
Terms of use
- Copyright holder:
- Pfister, T
- Copyright date:
- 2015
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