Skip to main content
SpringerLink
Log in

An Efficient Algorithm for Human Abnormal Behaviour Detection Using Object Detection and Pose Estimation

  • Conference paper
  • First Online:
The Fifth International Conference on Safety and Security with IoT

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

This paper presents a novel approach to detect and recognize human behaviour. The primary purpose of this work is to demonstrate the detection of abnormal behaviour within the restricted domain. The proposed work automatically detects the human being from the feed of video surveillance or regular videos. Once the human being is noticed in a frame, the human’s pose is estimated and the action is recognized. The action is then classified as normal or abnormal. The proposed work includes models such as the FAST R-CNN model for human detection within the video frame or image and the SSD MOBILE NET for human action recognition. The fast R-CNN model is one of the best algorithms for object detection and thus it is used to locate human beings within a specific frame. SSD-Mobilenet is used to estimate the human pose and recognize the action being performed. The combination of these two models makes the proposed model faster and more accurate. The proposed network is trained using the HMDB database. It is an extensive human motion database that consists of 51 kinds of actions. Results of the proposed work are better than state-of-the-art performance. The proposed work detects the human beings in a video-frame and then recognizes the action being performed by the human detected. Once the action is recognized, it is then classified as Normal or Abnormal actions. The proposed work can be used at various public places like metro stations, bus stands, banks, railway stations, theatres, etc. to prevent mishappening.

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 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
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

References

  1. Tayal, A., Kose, U., Solanki, A., Nayyar, A., Saucedo, J.A.M.: Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic, data envelopment analysis and machine learning. Comput. Intell. (2019). https://doi.org/10.1111/coin.12251

  2. Rajput, R., Solanki, A.: Review of sentimental analysis methods using lexicon based approach. Int. J. Comput. Sci. Mob. Comput. 5(2), 159–166 (2016)

    Google Scholar 

  3. Rajput, R., Solanki, A.: Real-time analysis of tweets using machine learning and semantic analysis. In: International Conference on Communication and Computing Systems (ICCCS-2016), Taylor and Francis, at Dronacharya College of Engineering, Gurgaon, 9–11 Sept, vol. 138(25), pp. 687–692 (2016)

    Google Scholar 

  4. Ashwan, A., Yu-Kun, L., Xianfang, S.: Saliency guided local and global descriptors for effective action recognition. Comput. Vis. Media. 2(1), 97–106 (2016)

    Article  Google Scholar 

  5. Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikäinen, M.: Deep learning. Int. J. Comput. Vis. 128, 261–318 (2020)

    Article  MATH  Google Scholar 

  6. Scovanner, P., Ali, S., Shah, M.: 3-dimensional SIFT descriptor and its application to action recognition. In: Proceedings of the 15th ACM International Conference on Multimedia, pp. 357–360 (2007)

    Chapter  Google Scholar 

  7. Priyadarshni, V., Nayyar, A., Solanki, A., Anuragi, A.: Human age classification system using K-NN classifier. In: Luhach, A., Jat, D., Hawari, K., Gao, X.Z., Lingras, P. (eds.) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol. 1075. Springer, Singapore (2019)

    Google Scholar 

  8. Willems, G., Tuytelaars, T., Van Gool, L.: An efficient dense and scale-invariant spatiotemporal interest point detector. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) Lecture Notes in Computer Science, vol. 5303, pp. 650–663. Springer, Berlin (2008)

    Google Scholar 

  9. Yuan, C., Li, X., Hu, W., Ling, H., Maybank, S.: 3D R transform on spatiotemporal interest points for action recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 724–730 (2013)

    Google Scholar 

  10. Zhang, H., Zhou, W., Reardon, C., Parker, L.: Simplex-based 3D spatio-temporal feature description for action recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2067–2074 (2014)

    Google Scholar 

  11. Chen, S., Jakeman, A., Norton, J.: Artificial Intelligence Techniques: An Introduction to Their Use for Modelling Environmental Systems. IMACS (2008)

    MATH  Google Scholar 

  12. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Article  Google Scholar 

  13. Taylor, W., Fergus, R., LeCun, Y., Bregler, C.: Convolutional learning of spatiotemporal features. In: Proceedings of the 11th European Conference on Computer Vision: Part VI, pp. 140–153 (2010)

    Google Scholar 

  14. Sun, X., Chen, M., Hauptmann, A.: Action recognition via local descriptors and holistic features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 58–65 (2009)

    Google Scholar 

  15. Tayal, A., Solanki, A., Singh, S.P.: Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102383

  16. Hueihan, J., Juergen, G., Silvia, Z., Cordelia, S., Michael, J.B.: Towards Understanding Action Recognition, pp. 1550–5499. IEEE, New York (2013)

    Google Scholar 

  17. Nasim, K., Mehdi, R.: Three-stream very deep neural network for video action recognition. In: 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), Tehran, Iran (2019)

    Google Scholar 

  18. Nicolas, B., Yi, Y., Zhen-zhong, L.: Space-time robust video representation for action recognition. IEEE Int. Conf. Comput. Vis. (2013)

    Google Scholar 

  19. Singh, T., Nayyar, A., Solanki, A.: Multilingual opinion mining movie recommendation system using RNN. In: Singh, P., Pawłowski, W., Tanwar, S., Kumar, N., Rodrigues, J., Obaidat, M. (eds.) Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) Lecture Notes in Networks and Systems, vol. 121. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3369-3_44

    Chapter  Google Scholar 

  20. Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and Binet–Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: IEEE Conference on Computer Vision and Pattern Recognition on Proceedings, pp. 1932–1939 (2009)

    Google Scholar 

  21. Hu, Y.: Design and implementation of abnormal behavior detection based on deep intelligent analysis algorithms in massive video surveillance. Journal of Grid Computing. 18 (2020). https://doi.org/10.1007/s10723-020-09506-2

  22. Popoola, O.P., Kejun, W.: Video-based abnormal human behavior recognition—a review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 865–878 (2012)

    Article  Google Scholar 

  23. Wang, T., Jie, C., Hichem, S.: Online detection of abnormal events in video streams. J. Elec. Comput. Eng. (2013)

    Google Scholar 

  24. Ahmed, Md, Mushfique I., Al-Amin A.: Dynamic image analysis for abnormal behavior detection, PhD diss., BRAC University (2017)

    Google Scholar 

  25. Xin, C., Yahong, H.: Multi-task CNN Model for Action Detection. IEEE, New York (2018)

    Google Scholar 

  26. Tasweer, A., Lianwen, J., Jialuo, F., Guozhi, T.: Human Action Recognition in Unconstrained Trimmed Videos Using Residual Attention Network and Joints Path Signature, vol. 7. IEEE, New York (2019)

    Google Scholar 

  27. Jianan, L., Xiaodan, L., Shengmei, S., Tingfa, X., Jiashi, F., Shuicheng, Y.: Scale-Aware Fast R-CNN for Pedestrian Detection. IEEE, New York (2017)

    Google Scholar 

  28. Ahuja, R., Solanki, A.: Movie recommender system using K-means clustering and K-nearest neighbor. In: Accepted for Publication in Confluence-2019: 9th International Conference on Cloud Computing, Data Science & Engineering, vol. 1231(21), pp. 25–38. Amity University, Noida (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Gautam Buddha University, Greater Noida, India

    Vaishnavi Narang & Arun Solanki

Authors
  1. Vaishnavi Narang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arun Solanki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Solanki .

Editor information

Editors and Affiliations

  1. School of Computer Science, Duy Tan University, Da Nang, Vietnam

    Anand Nayyar

  2. The School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Anand Paul

  3. Computer Science and Engineering, Nirma University, Gujarat, India

    Sudeep Tanwar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Narang, V., Solanki, A. (2023). An Efficient Algorithm for Human Abnormal Behaviour Detection Using Object Detection and Pose Estimation. In: Nayyar, A., Paul, A., Tanwar, S. (eds) The Fifth International Conference on Safety and Security with IoT . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-94285-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94285-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94284-7

  • Online ISBN: 978-3-030-94285-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation