Wavelets for Activity Recognition

  • Rajiv Singh
  • Swati Nigam
  • Amit Kumar Singh
  • Mohamed Elhoseny


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.


Behavior recognition Action Interaction Abnormal activities 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rajiv Singh
    • 1
  • Swati Nigam
    • 1
  • Amit Kumar Singh
    • 2
  • Mohamed Elhoseny
    • 3
  1. 1.Department of Computer ScienceBanasthali VidyapithBanasthaliIndia
  2. 2.Department of Computer Science & EngineeringNational Institute of TechnologyPatnaIndia
  3. 3.Faculty of Computers and InformationMansoura UniversityDakahliyaEgypt

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