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Enhancement of video streaming analysis using cluster-computing framework

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Abstract

Video content analysis is an emerging technique to easily redact video footage for public disclosure and to identify events and objects in surveillance cameras. The proficiency of this analysis depends on various crucial parameters such as area under exposure, content of surveillance and prior knowledge on statistical tool to enhance the streaming. There is large computational overhead in handling the huge quantum of data in traditional machine learning approach. In these scenario, the big data framework aids as a platform to handle the large volume of unstructured data. There is tremendous developments on big data framework which make new research avenue in data processing methods and big data learning paradigms. The proposed model analyses video streaming of a live traffic scenario over cluster computing framework in order to detect the anomalies. A video analytics model is developed with Spark execution engine to enable the data processing comparatively faster.

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Authors and Affiliations

  1. Department of Information Technology, Loyola-ICAM College of Engineering and Technology Loyola College Campus, Nungambakkam, Chennai, Tamilnadu, 600034, India

    Janani Arthanari

  2. Department of Computer Science, College of Engineering Guindy, Anna University, Chennai, Tamilnadu, 600025, India

    R. Baskaran

Authors
  1. Janani Arthanari
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  2. R. Baskaran
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Correspondence to Janani Arthanari.

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Arthanari, J., Baskaran, R. Enhancement of video streaming analysis using cluster-computing framework. Cluster Comput 22 (Suppl 2), 3771–3781 (2019). https://doi.org/10.1007/s10586-018-2284-y

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  • DOI: https://doi.org/10.1007/s10586-018-2284-y

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