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Wavelets for Activity Recognition

  • Rajiv Singh
  • Swati Nigam
  • Amit Kumar Singh
  • Mohamed Elhoseny
Chapter
  • 27 Downloads

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.

Keywords

Behavior recognition Action Interaction Abnormal activities 

References

  1. 1.
    Borges PVK, Conci N, Cavallaro A (2013) Video-based human behavior understanding: a survey. IEEE Trans Circuits Syst Video Technol 23(11):1993–2008CrossRefGoogle Scholar
  2. 2.
    Gonzàlez J, Moeslund TB, Wang L (2012) Semantic understanding of human behaviors in image sequences: from video-surveillance to video-hermeneutics. Comput Vis Image Underst 116(3):305–306CrossRefGoogle Scholar
  3. 3.
    Wiliem A, Madasu V, Boles W, Yarlagadda P (2012) A suspicious behaviour detection using a context space model for smart surveillance systems. Comput Vis Image Underst 116(2):194–209CrossRefGoogle Scholar
  4. 4.
    Nigam S, Singh R, Misra AK (2018) A review of computational approaches for human behavior detection. Arch Comput Methods Eng:1–33Google Scholar
  5. 5.
    Tran C, Doshi A, Trivedi MM (2012) Modeling and prediction of driver behavior by foot gesture analysis. Comput Vis Image Underst 116(3):435–445CrossRefGoogle Scholar
  6. 6.
    Vishwakarma S, Agrawal A (2013) A survey on activity recognition and behavior understanding in video surveillance. Vis Comput 29(10):983–1009CrossRefGoogle Scholar
  7. 7.
    Nigam S, Singh R, Misra AK (2019) Towards intelligent human behavior detection for video surveillance. In: Censorship, surveillance, and privacy: concepts, methodologies, tools, and applications. IGI Global, Hershey, pp 884–917CrossRefGoogle Scholar
  8. 8.
    Ziaeefard M, Bergevin R (2015) Semantic human activity recognition: a literature review. Pattern Recogn 48(8):2329–2345CrossRefGoogle Scholar
  9. 9.
    Aggarwal JK, Xia L (2014) Human activity recognition from 3d data: a review. Pattern Recogn Lett 48:70–80CrossRefGoogle Scholar
  10. 10.
    Yanan L, Kun JL, Yu YW (2014) Capturing human motion based on modified hidden markov model in multi-view image sequences. J Multimed 9(1):92–99Google Scholar
  11. 11.
    Binh NT, Nigam S, Khare A (2013) Towards classification based human activity recognition in video sequences. In: International conference on context-aware systems and applications. Springer, Cham, pp 209–218Google Scholar
  12. 12.
    Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247–2253CrossRefGoogle Scholar
  13. 13.
    Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: null. IEEE, pp 32–36Google Scholar
  14. 14.
    Bibi S, Anjum N, Sher M (2018) Automated multi-feature human interaction recognition in complex environment. Comput Ind 99:282–293CrossRefGoogle Scholar
  15. 15.
    Skibbe H, Reisert M, Schmidt T, Brox T, Ronneberger O, Burkhardt H (2012) Fast rotation invariant 3D feature computation utilizing efficient local neighborhood operators. IEEE Trans Pattern Anal Machine Intell 34(8):1563–1575CrossRefGoogle Scholar
  16. 16.
    Nigam S, Khare M, Srivastava RK, Khare A (2013) An effective local feature descriptor for object detection in real scenes. In: 2013 IEEE conference on information & communication technologies. IEEE, pp 244–248Google Scholar
  17. 17.
    Yussiff AL, Yong SP, Baharudin BB (2014) Detecting people using histogram of oriented gradients: a step towards abnormal human activity detection. In: Advances in computer science and its applications. Springer, Berlin/Heidelberg, pp 1145–1150CrossRefGoogle Scholar
  18. 18.
    Kong Y, Fu Y (2016) Close human interaction recognition using patch-aware models. IEEE Trans Image Process 25(1):167–178MathSciNetCrossRefGoogle Scholar
  19. 19.
    Cho NG, Park SH, Park JS, Park U, Lee SW (2017) Compositional interaction descriptor for human interaction recognition. Neurocomputing 267:169–181CrossRefGoogle Scholar
  20. 20.
    Liu C, Yuen J, Torralba A (2011) Sift flow: dense correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell 33(5):978–994CrossRefGoogle Scholar
  21. 21.
    Scovanner P, Ali S, Shah M (2007) A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th ACM international conference on multimedia. ACM, pp 357–360Google Scholar
  22. 22.
    Slimani KNEH, Benezeth Y, Souami F (2014) Human interaction recognition based on the co-occurence of visual words. In: IEEE CVPR CMSI workshop, pp 455–460Google Scholar
  23. 23.
    Nigam S, Khare A (2016) Integration of moment invariants and uniform local binary patterns for human activity recognition in video sequences. Multimed Tools Appl 75(24):17303–17332CrossRefGoogle Scholar
  24. 24.
    Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568–576Google Scholar
  25. 25.
    Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489–4497Google Scholar
  26. 26.
    Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal LSTM with trust gates for 3D human action recognition. In: European conference on computer vision. Springer, Cham, pp 816–833Google Scholar
  27. 27.
    Wang L, Qiao Y, Tang X (2015) Action recognition with trajectory-pooled deep-convolutional descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4305–4314Google Scholar
  28. 28.
    Alp Güler R, Neverova N, Kokkinos I (2018) Densepose: dense human pose estimation in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7297–7306Google Scholar
  29. 29.
    Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291–7299Google Scholar
  30. 30.
    Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI conference on artificial intelligenceGoogle Scholar
  31. 31.
    Piccardi M (2004) Background subtraction techniques: a review. In: 2004 IEEE international conference on systems, man and cybernetics (IEEE Cat. No. 04CH37583), vol 4. IEEE, pp 3099–3104Google Scholar
  32. 32.
    Uddin MZ, Lee JJ, Kim TS (2010) Independent shape component-based human activity recognition via Hidden Markov Model. Appl Intell 33(2):193–206CrossRefGoogle Scholar
  33. 33.
    Roshtkhari MJ, Levine MD (2012) A multi-scale hierarchical codebook method for human action recognition in videos using a single example. In: 2012 ninth conference on computer and robot vision. IEEE, pp 182–189Google Scholar
  34. 34.
    Ballan L, Bertini M, Del Bimbo A, Seidenari L, Serra G (2009) Human action recognition and localization using spatio-temporal descriptors and tracking. In: Proceedings of the workshop on pattern recognition and artificial intelligence for human behaviour analysis, Reggio Emilia, Italy, pp 1–8Google Scholar
  35. 35.
    Goudelis G, Karpouzis K, Kollias S (2013) Exploring trace transform for robust human action recognition. Pattern Recogn 46(12):3238–3248CrossRefGoogle Scholar
  36. 36.
    Roshtkhari MJ, Levine MD (2013) Human activity recognition in videos using a single example. Image Vis Comput 31(11):864–876CrossRefGoogle Scholar
  37. 37.
    Gupta JP, Singh N, Dixit P, Semwal VB, Dubey SR (2013) Human activity recognition using gait pattern. Int J Comput Vis Image Process 3(3):31–53CrossRefGoogle Scholar
  38. 38.
    Arunnehru J, Geetha MK (2013) Motion intensity code for action recognition in video using PCA and SVM. In: Mining intelligence and knowledge exploration. Springer, Cham, pp 70–81CrossRefGoogle Scholar

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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