Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Vision-Based Human Activity Recognition

Part of the book series: SpringerBriefs in Intelligent Systems ((BRIEFSINSY))

  • 293 Accesses

Abstract

This chapter gives an overview of human activity recognition as well as its commonly used sensors. We start from the background and challenges in vision-based human activity recognition. Then the related specific tasks are sorted out and the general solutions are briefly introduced. The instantiated tasks to be elaborated in the following chapters are finally discussed in a compact manner.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ding H, Yang X, Zheng N, Li M, Lai Y, Wu H (2018) Tri-co robot: a Chinese robotic research initiative for enhanced robot interaction capabilities. Natl Sci Rev 5(6):799–801

    Article  Google Scholar 

  2. Zhou J, Zhou Y, Wang B, Zang J (2019) Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering 5(4):624–636

    Article  Google Scholar 

  3. Committee SO-RAVS et al (2014) Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Standard J 3016:1–16

    Google Scholar 

  4. Stokel-Walker C (2022) Welcome to the metaverse. New Sci 253(3368):39–43

    Article  Google Scholar 

  5. Clarivate. Web of science. https://www.webofscience.com/wos/woscc/basic-search

  6. Hu Z-X, Wang Y, Ge M-F, Liu J (2020) Data-driven fault diagnosis method based on compressed sensing and improved multiscale network. IEEE Trans Ind Electron 67(4):3216–3225

    Article  Google Scholar 

  7. Liu J, Hu Y, Wang Y, Wu B, Fan J, Hu Z (2018) An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis. Measure Sci Technol 29(5):055103

    Google Scholar 

  8. Zhou K, Yang C, Liu J, Xu Q (2021) Dynamic graph-based feature learning with few edges considering noisy samples for rotating machinery fault diagnosis. IEEE Trans Ind Electron 1–1. https://doi.org/10.1109/TIE.2021.3121748

  9. Jha S, Marzban MF, Hu T, Mahmoud MH, Al-Dhahir N, Busso C (2021) The multimodal driver monitoring database: a naturalistic corpus to study driver attention. IEEE Trans Intell Transp Syst 1–17

    Google Scholar 

  10. Marcano M, Daz S, Prez J, Irigoyen E (2020) A review of shared control for automated vehicles: Theory and applications. IEEE Trans Hum-Mach Syst 50(6):475–491

    Article  Google Scholar 

  11. Zhang C, Eskandarian A (2021) A survey and tutorial of eeg-based brain monitoring for driver state analysis. IEEE/CAA J Automatica Sinica 8(7):1222–1242

    Article  Google Scholar 

  12. Katsigiannis S, Ramzan N (2017) Dreamer: a database for emotion recognition through eeg and ecg signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Inform 22(1):98–107

    Article  Google Scholar 

  13. Nmcov A, Svozilov V, Bucsuhzy K, Smek R, Mzl M, Hesko B, Belk M, Bilk M, Maxera P, Seitl M, Dominik T, Semela M, Ucha M, Kol R (2021) Multimodal features for detection of driver stress and fatigue: review. IEEE Trans Intell Transp Syst 22(6):3214–3233

    Google Scholar 

  14. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2. IEEE, pp 1150–1157

    Google Scholar 

  15. Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European conference on computer vision. Springer, pp 404–417

    Google Scholar 

  16. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 886–893

    Google Scholar 

  17. Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112

    Article  Google Scholar 

  18. Peng C-YJ, Lee KL, Ingersoll GM (2002) An introduction to logistic regression analysis and reporting. J Educ Res 96(1):3–14

    Article  Google Scholar 

  19. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567

    Article  Google Scholar 

  20. Sutton C, McCallum A (2006) An introduction to conditional random fields for relational learning. Introduction Stat Relat Learn 2:93–128

    Google Scholar 

  21. Wang JM, Fleet DJ, Hertzmann A (2007) Gaussian process dynamical models for human motion. IEEE Trans Pattern Anal Mach Intell 30(2):283–298

    Article  Google Scholar 

  22. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580–585

    Article  Google Scholar 

  23. Rasmussen C (1999) The infinite Gaussian mixture model. In: Advances in neural information processing systems, vol 12

    Google Scholar 

  24. Eddy SR (2004) What is a hidden markov model? Nat Biotechnol 22(10):1315–1316

    Article  Google Scholar 

  25. Ghahramani Z (1997) Learning dynamic Bayesian networks. In: International school on neural networks, initiated by IIASS and EMFCSC. Springer, pp 168–197

    Google Scholar 

  26. Boykov Y, Veksler O, Zabih R (1998) Markov random fields with efficient approximations. In: Proceedings. 1998 IEEE computer society conference on computer vision and pattern recognition (Cat. No. 98CB36231). IEEE, pp 648–655

    Google Scholar 

  27. Hopfield JJ (1988) Artificial neural networks. IEEE Circ Devices Mag 4(5):3–10

    Article  Google Scholar 

  28. Mikolov T, Karafiát M, Burget L, Cernockỳ, J, Khudanpur S (2010) Recurrent neural network based language model. In: Interspeech, vol 2. Makuhari, pp1045–1048

    Google Scholar 

  29. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  30. Ji S, Xu W, Yang M, Yu K (2012) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Zhongxu Hu & Chen Lv

Authors
  1. Zhongxu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongxu Hu .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hu, Z., Lv, C. (2022). Introduction. In: Vision-Based Human Activity Recognition. SpringerBriefs in Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-2290-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-2290-9_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2289-3

  • Online ISBN: 978-981-19-2290-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation