NDLI: Athlete pose estimation by non-sequential key-frame propagation

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Athlete pose estimation by non-sequential key-frame propagation

Content Provider	ACM Digital Library
Author	Hilton, Adrian Fastovets, Mykyta Guillemaut, Jean-Yves
Abstract	This paper considers the problem of estimating human pose in challenging monocular sports videos, where manual intervention is often required in order to obtain useful results. Fully automatic approaches focus on developing inference algorithms and probabilistic prior models based on learned measurements and often face challenges in generalisation beyond the learned dataset. This work expands on the idea of using an interactive model-based generative technique for accurately estimating the human pose from uncalibrated unconstrained monocular TV sports footage. A method of keyframe propagation is introduced to obtain reliable tracking from limited operator input by introducing the concepts of keyframe propagation and optimal keyframe selection assistance for the operator. Experimental results show that the approach produces results competitive with those produced with twice the number of manually annotated keyframes, halving the amount of interaction required.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781450331852
DOI	10.1145/2668904.2668938
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Computer vision Human pose-estimation Image similarity Sports
Content Type	Text
Resource Type	Article

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