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An Efficient Framework Based on Segmented Block Analysis for Human Activity Recognition

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Information and Decision Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 701))

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Abstract

Video surveillance systems core component is human activity recognition. Analysis and identification of human activity emphasize on understanding human behavior in the video. Human activity recognition aims to automatically conjecture the activity being acted by a person. In this paper, we propose a novel feature description algorithm in which a segmented block of logarithm-based motion-generating frames is normalized for analysis of action being performed in the image sequences. The features obtained are classified using random forest classifier. We evaluated the framework on HMDB-51 and ATM datasets and achieved an average accuracy of 58.24 and 93.57%.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era University, Dehradun, Uttarakhand, India

    Vikas Tripathi, Durgaprasad Gangodkar, Monika Pandey & Vishal Sanserwal

Authors
  1. Vikas Tripathi
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  2. Durgaprasad Gangodkar
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  3. Monika Pandey
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  4. Vishal Sanserwal
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Corresponding author

Correspondence to Vikas Tripathi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    Joao Manuel R.S. Tavares

  3. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Computer Application, KIIT University, Bhubaneswar, Odisha, India

    J. R. Mohanty

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Tripathi, V., Gangodkar, D., Pandey, M., Sanserwal, V. (2018). An Efficient Framework Based on Segmented Block Analysis for Human Activity Recognition. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_42

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  • DOI: https://doi.org/10.1007/978-981-10-7563-6_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7562-9

  • Online ISBN: 978-981-10-7563-6

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