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2012
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Exemplar-based human action pose correction and tagging

6 years 9 months ago
Exemplar-based human action pose correction and tagging
The launch of Xbox Kinect has built a very successful computer vision product and made a big impact to the gaming industry; this sheds lights onto a wide variety of potential applications related to action recognition. The accurate estimation of human poses from the depth image is universally a critical step. However, existing pose estimation systems exhibit failures when faced severe occlusion. In this paper, we propose an exemplar-based method to learn to correct the initially estimated poses. We learn an inhomogeneous systematic bias by leveraging the exemplar information within specific human action domain. Our algorithm is illustrated on both joint-based skeleton correction and tag prediction. In the experiments, significant improvement is observed over the contemporary approaches, including what is delivered by the current Kinect system.
Wei Shen, Ke Deng, Xiang Bai, Tommer Leyvand, Bain
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Wei Shen, Ke Deng, Xiang Bai, Tommer Leyvand, Baining Guo, Zhuowen Tu
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