Thesis icon

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...

Expand abstract

Actions


Access Document


Files:

Authors


More by this author
Institution:
University of Oxford
Research group:
Visual Geometry Group
Oxford college:
Wolfson College
Department:
Mathematical,Physical & Life Sciences Division - Engineering Science

Contributors

Role:
Supervisor
Publication date:
2015
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
URN:
uuid:64e5b1be-231e-49ed-b385-e87db6dbeed8
Local pid:
ora:11908

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP