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    Video stream analysis in clouds: An object detection and classification framework for high performance video analytics

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    Authors
    Anjum, Ashiq cc
    Abdullah, Tariq
    Tariq, M. Fahim
    Baltaci, Yusuf
    Antonopoulos, Nikolaos
    Affiliation
    University of Derby, UK
    Issue Date
    2016-01-13
    
    Metadata
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    Abstract
    Email Print Request Permissions Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers an operator by automating the object detection and classification process from recorded video streams. An operator only specifies an analysis criteria and duration of video streams to analyse. The streams are then fetched from a cloud storage, decoded and analysed on the cloud. The framework executes compute intensive parts of the analysis to GPU powered servers in the cloud. Vehicle and face detection are presented as two case studies for evaluating the framework, with one month of data and a 15 node cloud. The framework reliably performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled deployment of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least twice as fast than the cloud deployment without GPUs.
    Citation
    Anjum, A. et al., 'Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics', 2016:1, IEEE Transactions on Cloud Computing
    Publisher
    IEEE
    Journal
    IEEE Transactions on Cloud Computing
    URI
    http://hdl.handle.net/10545/617828
    DOI
    10.1109/TCC.2016.2517653
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7381631
    Type
    Article
    Language
    en
    ISSN
    2168-7161
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCC.2016.2517653
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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