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Shahin Mehdipour Ataee, Ali Broumandnia. Identifying Periodical Activities Independent of Video Content[J]. Journal of Electronic Science and Technology, 2014, 12(1): 122-129. DOI: 10.3969/j.issn.1674-862X.2014.01.024
Citation: Shahin Mehdipour Ataee, Ali Broumandnia. Identifying Periodical Activities Independent of Video Content[J]. Journal of Electronic Science and Technology, 2014, 12(1): 122-129. DOI: 10.3969/j.issn.1674-862X.2014.01.024
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Identifying Periodical Activities Independent of Video Content

doi: 10.3969/j.issn.1674-862X.2014.01.024
  • 1.

    Department of Computer Engineering,Eastern Mediterranean University,TRNC via Mersin10,Turkey;

  • 2.

    Department of Computer Engineering,Islamic Azad University—South Tehran Branch,Tehran,Iran

More Information
  • Author Bio:

    Shahin Mehdipour Ataee research interests include video analysis, pattern recognition, cloud computing, and web services, shahin.mehdipour@emu.edu.tr;
    Ali Broumandnia, broumandnia@gmail.com

    Shahin Mehdipour Ataee research interests include video analysis, pattern recognition, cloud computing, and web services, shahin.mehdipour@emu.edu.tr;
    Ali Broumandnia, broumandnia@gmail.com

  • Authors’ information: Shahin Mehdipour Ataee
  • Received Date: 2013-05-27
  • Rev Recd Date: 2013-06-29
  • Publish Date: 2014-03-24
    Abstract
  • In this research, a completely new and accurate method has been presented for detecting periodic activities with the help of machine vision. The proposed method is independent of motion tracking complex algorithms unlike the previous strategies and it is fully independent of contents and types of activities by performing low level calculation. Not using of heavy computations while improving the ability of periodicity detection is regarded as the unique feature of this method. The use of general and flexible framework in this method causes to facilitate the machine vision periodic activities identification process.
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