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Wavelets for Activity Recognition

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Intelligent Wavelet Based Techniques for Advanced Multimedia Applications

Abstract

This chapter analyses real world human activity recognition problem. It uses discrete wavelet transform and multiclass support vector machine (SVM) classifier for recognition. The experiments are done using Weizmann and KTH action datasets. Objective evaluation is done for nine activities walk, run, bend, gallop sideways, jumping jack, one handwave, two handwave, jump in place and skip from Weizmann dataset. Six activities that are considered from KTH dataset are handwaving, running, walking, boxing, jogging and handclapping. Results are shown qualitatively as well as quantitatively on two publicly available dataset Weizmann and KTH. Quantitative evaluations demonstrate better performance of the proposed method in comparison to the existing methods.

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Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Banasthali, Rajasthan, India

    Rajiv Singh & Swati Nigam

  2. Department of Computer Science & Engineering, National Institute of Technology, Patna, Bihar, India

    Amit Kumar Singh

  3. Faculty of Computers and Information, Mansoura University, Dakahliya, Egypt

    Mohamed Elhoseny

Authors
  1. Rajiv Singh
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  2. Swati Nigam
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  3. Amit Kumar Singh
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  4. Mohamed Elhoseny
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Singh, R., Nigam, S., Singh, A.K., Elhoseny, M. (2020). Wavelets for Activity Recognition. In: Intelligent Wavelet Based Techniques for Advanced Multimedia Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-31873-4_8

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  • DOI: https://doi.org/10.1007/978-3-030-31873-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31872-7

  • Online ISBN: 978-3-030-31873-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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