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    SCC: Semantic Context Cascade for Efficient Action Detection

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    Type
    Conference Paper
    Authors
    Heilbron, Fabian Caba
    Barrios, Wayner
    Escorcia, Victor cc
    Ghanem, Bernard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2017-11-09
    Online Publication Date
    2017-11-09
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/626983
    
    Metadata
    Show full item record
    Abstract
    Despite 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.
    Citation
    Heilbron 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.
    Sponsors
    Research in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    Conference/Event name
    30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    DOI
    10.1109/CVPR.2017.338
    Additional Links
    http://ieeexplore.ieee.org/document/8099821/
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR.2017.338
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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