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Conference Paper: A two-stage detector for hand detection in ego-centric videos

TitleA two-stage detector for hand detection in ego-centric videos
Authors
Issue Date2016
PublisherIEEE. The Journal's web site is located at ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000040
Citation
The 2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016), Lake Placid, NY., 7-10 March 2016. In Conference Proceedings, 2016, p. 1-8 How to Cite?
AbstractWe propose a two-stage detector that can not only detect and localize hands, but also provide fine-detailed information in the bounding box of hand in an efficient fashion. In the first stage, hand bounding box proposals are generated from a pixel-level hand probability map. Next, each hand proposal is evaluated by a Multi-task Convolutional Neural Network to filter out false positives and obtain fine shape and landmark information. Through experiments, we demonstrate that our method is efficient and robust to detect hands with their shape and landmark information, and our system can also be flexibly combined with other detection methods to handle a new scene. Further experiment shows that our Multi-task CNN can also be extended to hand gesture classification with a large performance increase. © 2016 IEEE.
DescriptionSession: Oral 2D: Aerial/Mobile
Persistent Identifierhttp://hdl.handle.net/10722/225691
ISBN

 

DC FieldValueLanguage
dc.contributor.authorZhu, X-
dc.contributor.authorLiu, W-
dc.contributor.authorJia, X-
dc.contributor.authorWong, KKY-
dc.date.accessioned2016-05-20T08:10:08Z-
dc.date.available2016-05-20T08:10:08Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016), Lake Placid, NY., 7-10 March 2016. In Conference Proceedings, 2016, p. 1-8-
dc.identifier.isbn978-150900641-0-
dc.identifier.urihttp://hdl.handle.net/10722/225691-
dc.descriptionSession: Oral 2D: Aerial/Mobile-
dc.description.abstractWe propose a two-stage detector that can not only detect and localize hands, but also provide fine-detailed information in the bounding box of hand in an efficient fashion. In the first stage, hand bounding box proposals are generated from a pixel-level hand probability map. Next, each hand proposal is evaluated by a Multi-task Convolutional Neural Network to filter out false positives and obtain fine shape and landmark information. Through experiments, we demonstrate that our method is efficient and robust to detect hands with their shape and landmark information, and our system can also be flexibly combined with other detection methods to handle a new scene. Further experiment shows that our Multi-task CNN can also be extended to hand gesture classification with a large performance increase. © 2016 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000040-
dc.relation.ispartofIEEE Winter Conference on Applications of Computer Vision Proceedings-
dc.rightsIEEE Winter Conference on Applications of Computer Vision Proceedings. Copyright © IEEE.-
dc.rights©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA two-stage detector for hand detection in ego-centric videos-
dc.typeConference_Paper-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.authorityWong, KKY=rp01393-
dc.description.naturepostprint-
dc.identifier.doi10.1109/WACV.2016.7477665-
dc.identifier.scopuseid_2-s2.0-84977667888-
dc.identifier.hkuros258052-
dc.identifier.hkuros263060-
dc.identifier.spage1-
dc.identifier.epage8-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160923-

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