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    Cloud-based video analytics using convolutional neural networks.

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    Authors
    Yaseen, Muhammad Usman cc
    Anjum, Ashiq cc
    Farid, Mohsen
    Antonopoulos, Nick
    Affiliation
    University of Derby
    Issue Date
    2018-09-13
    
    Metadata
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    Abstract
    Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of distributed processing for training and inference. We propose a cloud‐based video analytics system based on an optimally tuned convolutional neural network to classify objects from video streams. The tuning of convolutional neural network is empowered by in‐memory distributed computing. The object classification is performed by comparing the target object with the prestored trained patterns, generating a set of matching scores. The matching scores greater than an empirically determined threshold reveal the classification of the target object. The proposed system proved to be robust to classification errors with an accuracy and precision of 97% and 96%, respectively, and can be used as a general‐purpose video analytics system.
    Citation
    Yaseen, M.U., Anjum A, Farid M, Antonopoulos N. (2018) 'Cloud‐based video analytics using convolutional neural networks', Software: Practice and Experience. DOI: 10.1002/spe.2636
    Journal
    Software: Practice and Experience
    URI
    http://hdl.handle.net/10545/623037
    DOI
    10.1002/spe.2636
    Additional Links
    http://doi.wiley.com/10.1002/spe.2636
    Type
    Article
    Language
    en
    ISSN
    0038-0644
    ae974a485f413a2113503eed53cd6c53
    10.1002/spe.2636
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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