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Conference Paper: Pixel-level hand detection with shape-aware structured forests

TitlePixel-level hand detection with shape-aware structured forests
Authors
Issue Date2014
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 12th Asian Conference on Computer Vision, Singapore, 1-5 November 2014. In Lecture Notes in Computer Science, 2014, v. 9006, p. 64-78 How to Cite?
AbstractHand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask for a pixel using structured forests. This approach can better exploit hand shape information in the training data, and enforce shape constraints in the estimation. Aggregation of multiple predictions generated from neighboring pixels further improves the robustness of our method. We evaluate our method on both ego-centric videos and unconstrained still images. Experiment results show that our method can detect hands efficiently and outperform other state-of-the-art methods.
DescriptionLNCS v. 9006 entitled: Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV
Persistent Identifierhttp://hdl.handle.net/10722/219220
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorZhu, X-
dc.contributor.authorJia, X-
dc.contributor.authorWong, KKY-
dc.date.accessioned2015-09-18T07:18:04Z-
dc.date.available2015-09-18T07:18:04Z-
dc.date.issued2014-
dc.identifier.citationThe 12th Asian Conference on Computer Vision, Singapore, 1-5 November 2014. In Lecture Notes in Computer Science, 2014, v. 9006, p. 64-78-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/219220-
dc.descriptionLNCS v. 9006 entitled: Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV-
dc.description.abstractHand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask for a pixel using structured forests. This approach can better exploit hand shape information in the training data, and enforce shape constraints in the estimation. Aggregation of multiple predictions generated from neighboring pixels further improves the robustness of our method. We evaluate our method on both ego-centric videos and unconstrained still images. Experiment results show that our method can detect hands efficiently and outperform other state-of-the-art methods.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Science-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16817-3_5-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titlePixel-level hand detection with shape-aware structured forests-
dc.typeConference_Paper-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.authorityWong, KKY=rp01393-
dc.description.naturepostprint-
dc.identifier.doi10.1007/978-3-319-16817-3_5-
dc.identifier.hkuros252038-
dc.identifier.volume9006-
dc.identifier.spage64-
dc.identifier.epage78-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 151216-

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