Skip to main content
SpringerLink
Log in

A Study on Vision-Based Human Activity Recognition Approaches

  • Conference paper
  • First Online:
Modeling, Simulation and Optimization (CoMSO 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 373))

Included in the following conference series:

  • 137 Accesses

Abstract

The objective of human activity recognition (HAR) is to categorize actions from subject behavior and environmental factors. Systems for automatically identifying and analyzing human activities make use of data collected from many types of sensors. Despite the fact that numerous in-depth review articles on general HAR themes have already been published, the area requires ongoing updates due to the developing technology and multidisciplinary nature. This study makes an effort to recapitulate the development of HAR from computer vision standpoint. HAR tasks are significantly associated to the majority of computer vision applications, including surveillance, security, virtual reality, and smart home. The improvements of cutting-edge activity recognition techniques are highlighted in this review, particularly for the activity representation and classification approaches. Research timeline is organized on the basis of representation techniques. We discuss a number of widely used approaches for classification and adhere on the category of discriminative, template-oriented, and generative models. This study also focuses on the major drawbacks and potential solutions.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. https://www.image-net.org/index.php

  2. Zhao, C., Chen, M., Zhao, J., Wang, Q., Shen, Y.: 3d behavior recognition based on multi-modal deep space-time learning. Appl. Sci. 9(4), 716 (2019)

    Article  Google Scholar 

  3. Khan, M.A., Sharif, M., Akram, T., Raza, M., Saba, T., Rehman, A.: Hand-crafted and deep convolutional neural network features fusion and selection strategy: an application to intelligent human action recognition. Appl. Soft Comput. 87, 105986 (2020)

    Article  Google Scholar 

  4. Wang, L., Xu, Y., Yin, J., Wu, J.: Human action recognition by learning spatio-temporal features with deep neural networks. IEEE Access 6, 17913–17922 (2018)

    Article  Google Scholar 

  5. Ji, X., Zhao, Q., Cheng, J., Ma, C.: Exploiting spatio-temporal representation for 3D human action recognition from depth map sequences. Knowl. Based Syst. 227 (2021)

    Google Scholar 

  6. Li, C., Zhong, Q., Xie, D., Pu, S.: Collaborative spatiotemporal feature learning for video action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7872–7881 (2019)

    Google Scholar 

  7. Wang, Q., Sun, G., Dong, J., Ding, Z.: Continuous multi-view human action recognition. IEEE Trans. Circ. Syst. Video Technol. (2021)

    Google Scholar 

  8. Khelalef, A., Benoudjit, N.: An efficient human activity recognition technique based on deep learning. Pattern Recognit. Image Anal. 29(4), 702–715 (2019)

    Article  Google Scholar 

  9. Sahoo, S.P., Srinivasu, U., Ari, S.: 3D Features for human action recognition with semi-supervised learning. IET Image Proc. 13(6), 983–990 (2019)

    Article  Google Scholar 

  10. Baradel, F., Wolf, C., Mille, J., Taylor, G.W.: Glimpse clouds: human activity recognition from unstructured feature points. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 469–478 (2018)

    Google Scholar 

  11. Vishwakarma, D.K., Dhiman, C.: A unified model for human activity recognition using spatial distribution of gradients and difference of Gaussian kernel. Vis. Comput. 35(11), 1595–1613 (2019)

    Article  Google Scholar 

  12. Bulbul, M.F., Tabussum, S., Ali, H., Zheng, W., Lee, M.Y., Ullah, A.: Exploring 3D human action recognition using STACOG on multi-view depth motion maps sequences. Sensors 21(11), 3642 (2021)

    Article  Google Scholar 

  13. Arunnehru, J., Thalapathiraj, S., Dhanasekar, R., Vijayaraja, L., Kannadasan, R., Khan, A.A., Haq, M.A., Alshehri, M., Alwanain, M.I., Keshta, I.: Machine vision-based human action recognition using spatio-temporal motion features (STMF) with difference intensity distance group pattern (DIDGP). Electronics 11(15), 2363 (2022)

    Google Scholar 

  14. Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1817–1824. IEEE (2011, May)

    Google Scholar 

  15. Silva, M.V., Marana, A.N.: Human action recognition in videos based on spatiotemporal features and bag-of-poses. Appl. Soft Comput. 95, 106513 (2020)

    Article  Google Scholar 

  16. Huang, N., Liu, Y., Zhang, Q., Han, J.: Joint cross-modal and unimodal features for RGB-D salient object detection. IEEE Trans. Multimedia 23, 2428–2441 (2020)

    Article  Google Scholar 

  17. Yao, H., Yang, M., Chen, T., Wei, Y., Zhang, Y.: Depth-based human activity recognition via multi-level fused features and fast broad learning system. Int. J. Distrib. Sens. Netw. 16(2) (2020)

    Google Scholar 

  18. Kumar, N.: Better performance in human action recognition from spatiotemporal depth information features classification. In: Computational Network Application Tools for Performance Management, pp. 39–51. Springer, Singapore (2020)

    Google Scholar 

  19. Yang, X., Tian, Y.: Super normal vector for human activity recognition with depth cameras. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1028–1039 (2017)

    Article  Google Scholar 

  20. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern, pp. 9–14, San Francisco, CA, USA (2016)

    Google Scholar 

  21. Franco, A., Magnani, A., Maio, D.: A multimodal approach for human activity recognition based on skeleton and RGB data. Pattern Recogn. Lett. 131, 293–299 (2020)

    Article  Google Scholar 

  22. Liu, J., Wang, Z., Liu, H.: HDS-SP: a novel descriptor for skeleton-based human action recognition. Neurocomputing 385, 22–32 (2020)

    Article  Google Scholar 

  23. Li, G., Li, C.: Learning skeleton information for human action analysis using Kinect. Sig. Process. Image Commun. 84, 115814 (2020)

    Article  Google Scholar 

  24. Zhu, W., Lan, C., Xing, J., et al.: Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks, vol. 2, p. 8 (2016). arXiv Preprint

    Google Scholar 

  25. Shahroudy, A., Ng, T.T., Yang, Q., Wang, G.: Multimodal multipart learning for action recognition in depth videos. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2123–2129 (2016)

    Article  Google Scholar 

  26. Chen, H., Wang, G., Xue, J.H., He, L.: A novel hierarchical framework for human action recognition. Pattern Recogn. 55, 148–159 (2016)

    Article  Google Scholar 

  27. Sun, B., Kong, D., Wang, S., Wang, L., Yin, B.: Joint transferable dictionary learning and view adaptation for multi-view human action recognition. ACM Trans. Knowl. Discovery Data (TKDD) 15(2), 1–23 (2021)

    Article  Google Scholar 

  28. Xu, K., Qin, Z., Wang, G.: Recognize human activities from multi-part missing videos. In: IEEE International Conference on Multimedia and Expo, ICME 2016, pp. 976–990 (2016)

    Google Scholar 

  29. Mojarad, R., Attal, F., Chibani, A., Amirat, Y.: Automatic classification error detection and correction for robust human activity recognition. IEEE Robot. Autom. Lett. 5(2), 2208–2215 (2020)

    Article  Google Scholar 

  30. Suk, H.I., Sin, B.K., Lee, S.W.: Hand gesture recognition based on dynamic Bayesian network framework. Pattern Recogn. 43, 3059–3072 (2016)

    Article  Google Scholar 

  31. Hartmann, Y., Liu, H., Lahrberg, S., Schultz, T.: Interpretable high-level features for human activity recognition. In: BIOSIGNALS, pp. 40–49 (2022)

    Google Scholar 

  32. Rodriguez Lera, F.J., Martin Rico, F., Guerrero Higueras, A.M., Olivera, V.M.: A context awareness model for activity recognition in robot assisted scenarios. Expert. Syst. 37(2), e12481 (2020)

    Article  Google Scholar 

  33. Gedamu, K., Ji, Y., Yang, Y., Gao, L., Shen, H.T.: Arbitrary-view human action recognition via novel-view action generation. Pattern Recogn. 118, 108043 (2021)

    Article  Google Scholar 

  34. Pramono, R.R.A., Chen, Y.T., Fang, W.H.: Empowering relational network by self-attention augmented conditional random fields for group activity recognition. In: European Conference on Computer Vision, pp. 71–90. Springer, Cham (2020, Aug)

    Google Scholar 

  35. Liu, W., Piao, Z., Tu, Z., Luo, W., Ma, L., Gao, S.: Liquid warping GAN with attention: a unified framework for human image synthesis. IEEE Trans. Pattern Anal. Mach. Intell. (2021)

    Google Scholar 

  36. Agahian, S., Negin, F., Köse, C.: An efficient human action recognition framework with pose-based spatiotemporal features. Int. J. Eng. Sci. Technol. 23(1), 196–203 (2020)

    Google Scholar 

  37. Basly, H., Ouarda, W., Sayadi, F.E., Ouni, B., Alimi, A.M.: DTR-HAR: deep temporal residual representation for human activity recognition. Vis. Comput. 38(3), 993–1013 (2022)

    Article  Google Scholar 

  38. Gaikwal, R.S., Admuthe, L.S.: A review of various sign language techniques. In: Conference Proceedings of COSMO 2021, SIST. Springer (2021)

    Google Scholar 

  39. Swathi, K., Rao, J.N., Gargi, M., VaniSri, K.L., Shyamala, B.: Human activities recognition using OpenCV and deep learning techniques. Int. J. Future Gener. Commun. Netw. 13(3), 717–724 (2020)

    Google Scholar 

  40. Chaquet, J.M., Carmona, E.J., Fernández-Caballero, A.: A survey of video datasets for human action and activity recognition. Comput. Vis. Image Underst. 117, 633–659 (2013)

    Article  Google Scholar 

  41. https://sites.google.com/view/wanqingli/data-sets/msr-dailyactivity3d

Download references

Author information

Authors and Affiliations

  1. LBS Institute of Technology for Women, Marian Engineering College, APJ Abdul Kalam Technological University, Poojappura, Thiruvananthapuram, Kerala, India

    S. L. Reeja

  2. College of Engineering Muttathara, Thiruvananthapuram, Kerala, India

    T. Soumya

  3. LBS Institute of Technology for Women, Poojappura, India

    P. S. Deepthi

Authors
  1. S. L. Reeja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Soumya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. S. Deepthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. L. Reeja .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Cachar, Assam, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, Assam, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Cachar, India

    Sivaji Bandyopadhyay

  4. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  5. Department of Mechanical Engineering, Curtin University, Miri, Malaysia

    Sukanta Roy

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Reeja, S.L., Soumya, T., Deepthi, P.S. (2024). A Study on Vision-Based Human Activity Recognition Approaches. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E., Roy, S. (eds) Modeling, Simulation and Optimization. CoMSO 2022. Smart Innovation, Systems and Technologies, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-99-6866-4_17

Download citation

Publish with us

Policies and ethics

Search

Navigation