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    Deep learning hyper-parameter optimization for video analytics in clouds.

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    Authors
    Yaseen, Muhammad Usman cc
    Anjum, Ashiq cc
    Rana, Omer cc
    Antonopoulos, Nikolaos
    Affiliation
    University of Derby
    Cardiff University
    Issue Date
    2018-06-15
    
    Metadata
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    Abstract
    A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematical model used to support hyper-parameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system's performance is compared. Subsequently, the parameters that contribute toward the most optimal performance are selected for the video object classification pipeline. Our experiment-based validation reveals an accuracy and precision of 97% and 96%, respectively. The system proved to be scalable, robust, and customizable for a variety of different applications.
    Citation
    Yaseen, M. U. et al (2018) 'Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds', IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2018.2840341
    Publisher
    IEEE
    Journal
    IEEE Transactions on Systems, Man, and Cybernetics
    URI
    http://hdl.handle.net/10545/622767
    DOI
    10.1109/TSMC.2018.2840341
    Additional Links
    https://ieeexplore.ieee.org/document/8386680/
    Type
    Article
    Language
    en
    ISSN
    21682216
    EISSN
    21682232
    ae974a485f413a2113503eed53cd6c53
    10.1109/TSMC.2018.2840341
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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