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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Appearance Sharing for Collective Human Pose Estimation
verfasst von : Marcin Eichner, Vittorio Ferrari
Erschienen in: Computer Vision – ACCV 2012
Verlag: Springer Berlin Heidelberg
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While human pose estimation (HPE) techniques usually process each test image independently, in real applications images come in collections containing interdependent images. Often several images have similar backgrounds or show persons wearing similar clothing (foreground). We present a novel human pose estimation technique to exploit these dependencies by sharing appearance models between images. Our technique automatically determines which images in the collection should share appearance. We extend the state-of-the art HPE model of Yang and Ramanan to include our novel appearance sharing cues and demonstrate on the highly challenging Leeds Sports Poses dataset that they lead to better results than traditional single-image pose estimation.