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dc.contributor.authorChen, Jiaweien_US
dc.contributor.authorWu, Jonathanen_US
dc.contributor.authorKonrad, Januszen_US
dc.contributor.authorIshwar, Prakashen_US
dc.coverage.spatialSanta Rosa, CAen_US
dc.date.accessioned2019-11-13T16:52:58Z
dc.date.available2019-11-13T16:52:58Z
dc.date.issued2017-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000404165800016&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationJiawei Chen, Jonathan Wu, Janusz Konrad, Prakash Ishwar. 2017. "Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions." 2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017). 17th IEEE Winter Conference on Applications of Computer Vision (WACV). Santa Rosa, CA, 2017-03-24 - 2017-03-31. https://doi.org/10.1109/WACV.2017.23
dc.identifier.issn2472-6737
dc.identifier.urihttps://hdl.handle.net/2144/38498
dc.description.abstractDeep convolutional neural networks (ConvNets) have been recently shown to attain state-of-the-art performance for action recognition on standard-resolution videos. However, less attention has been paid to recognition performance at extremely low resolutions (eLR) (e.g., 16 12 pixels). Reliable action recognition using eLR cameras would address privacy concerns in various application environments such as private homes, hospitals, nursing/rehabilitation facilities, etc. In this paper, we propose a semi-coupled, filter-sharing network that leverages high-resolution (HR) videos during training in order to assist an eLR ConvNet. We also study methods for fusing spatial and temporal ConvNets customized for eLR videos in order to take advantage of appearance and motion information. Our method outperforms state-of-the-art methods at extremely low resolutions on IXMAS (93:7%) and HMDB (29:2%) datasets.en_US
dc.format.extent139 - 147en_US
dc.languageEnglish
dc.publisherIEEEen_US
dc.relation.ispartof2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017)
dc.subjectComputer science, artificial intelligenceen_US
dc.subjectEngineering, electrical & electronicen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.titleSemi-coupled two-stream fusion ConvNets for action recognition at extremely low resolutionsen_US
dc.typeConference materialsen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1109/WACV.2017.23
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv104175


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