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dc.contributor.authorHeilbron, Fabian Caba
dc.contributor.authorBarrios, Wayner
dc.contributor.authorEscorcia, Victor
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2018-02-01T07:25:01Z
dc.date.available2018-02-01T07:25:01Z
dc.date.issued2017-11-09
dc.identifier.citationHeilbron FC, Barrios W, Escorcia V, Ghanem B (2017) SCC: Semantic Context Cascade for Efficient Action Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/CVPR.2017.338.
dc.identifier.doi10.1109/CVPR.2017.338
dc.identifier.urihttp://hdl.handle.net/10754/626983
dc.description.abstractDespite the recent advances in large-scale video analysis, action detection remains as one of the most challenging unsolved problems in computer vision. This snag is in part due to the large volume of data that needs to be analyzed to detect actions in videos. Existing approaches have mitigated the computational cost, but still, these methods lack rich high-level semantics that helps them to localize the actions quickly. In this paper, we introduce a Semantic Cascade Context (SCC) model that aims to detect action in long video sequences. By embracing semantic priors associated with human activities, SCC produces high-quality class-specific action proposals and prune unrelated activities in a cascade fashion. Experimental results in ActivityNet unveils that SCC achieves state-of-the-art performance for action detection while operating at real time.
dc.description.sponsorshipResearch in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8099821/
dc.titleSCC: Semantic Context Cascade for Efficient Action Detection
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journal2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
dc.conference.dateJUL 21-26, 2016
dc.conference.name30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
dc.conference.locationHonolulu, HI
kaust.personHeilbron, Fabian Caba
kaust.personBarrios, Wayner
kaust.personEscorcia, Victor
kaust.personGhanem, Bernard
dc.date.published-online2017-11-09
dc.date.published-print2017-07


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