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Motion Intensity Code for Action Recognition in Video Using PCA and SVM

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Mining Intelligence and Knowledge Exploration

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8284))

Abstract

Manual video surveillance is highly expensive and inconvenient in continuous monitoring, by a security personnel. So automatic video surveillance and activity recognition is needed. In this paper, an activity recognition approach is proposed, the difference image is used to extract the motion information based on Region of Interest (ROI). The experiments are carried out on KTH dataset, considering four activities viz (walking, running, waving and boxing) and Weizmann dataset, considering four activities viz (walking, running, waving one hand, waving both hands) with Support Vector Machines (SVM) for classification. This approach shows an overall performance of 94.75% using KTH dataset and 92% using Weizmann dataset to recognize the actions. The performance of the proposed approach is comparable with well known existing methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, India

    J. Arunnehru & M. Kalaiselvi Geetha

Authors
  1. J. Arunnehru
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  2. M. Kalaiselvi Geetha
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Editor information

Editors and Affiliations

  1. Department of Business Information Systems, FSIC, National University of Ireland, University College Cork, O’Rahilly Buildings, Cork, Ireland

    Rajendra Prasath

  2. Research Centre in Computer Science, V.H.N.Senthikumara Nadar College (Autonomous), 626 001, Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Arunnehru, J., Geetha, M.K. (2013). Motion Intensity Code for Action Recognition in Video Using PCA and SVM. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-03844-5_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03843-8

  • Online ISBN: 978-3-319-03844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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