Skip to main content
Springer Nature Link
Log in

Elderly Patient Fall Detection Using Video Surveillance

  • Conference paper
  • First Online:
Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

Included in the following conference series:

Abstract

The important goal of this work is to design a system for elderly people who are at higher risk of falling due to some illness. We aim to do this without burdening them with the different number of devices around them including wear-able devices. We aim to continuously monitor them in a closed environment for any kind of falls or injuries that might occur to them. This paper aims to achieve this with the help of only video surveillance [13, 16]. We also aim to propose a method where the model is very cost-friendly and easy to implement in any closed environment. Our model removes any use of wearable sensors and proposes an approach where we use inputs from only RGB camera-based sensors and then uses different computer vision approaches [2, 6] to decide if the person has fallen or not. We are using a two-stream network to attain more accuracy. Estimating the motion of a person on regular basis is the main step in the proposed method. There are two most popular publically available methods for motion detection namely Optical Flow (OF) [26] and Motion History Image (MHI) [17]. In the proposed method, we are using OF for the motion estimation. With the help of OF [26] and VGG16 [25] we have proposed a method for elderly patient monitoring system using live camera to detect a falling person. Despite not using multiple wearable and non-wearable sensors our proposed method outperforms the existing fall detection models. We have worked on the publically available UR-Fall Dataset [10].

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, J., Lockhart, T.E.: Development and evaluation of a prior-to-impact fall event detection algorithm. IEEE Trans. Biomed. Eng. 61(7), 2135–2140 (2014). https://doi.org/10.1109/TBME.2014.2315784

    Article  Google Scholar 

  2. Chaudhary, S. Deep learning approaches to tackle the challenges of human action recognition in videos. Dissertation (2019)

    Google Scholar 

  3. Patil, P.W., Dudhane, A., Chaudhary, S., Murala, S.: Multi-frame based adversarial learning approach for video surveillance. Pattern Recognit. 122, 108350 (2022)

    Article  Google Scholar 

  4. Yazar, A., Erden, F., Cetin, A.: Multi-sensor ambient assisted living system for fall detection (2014)

    Google Scholar 

  5. Agrawal, S.C., Tripathi, R.K., Jalal, A.S.: Human-fall detection from an in-door video surveillance. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, pp. 1–5 (2017). https://doi.org/10.1109/ICCCNT.2017.8203923.

  6. Chaudhary, S., Dudhane, A., Patil, P., Murala, S.: Pose guided dynamic image network for human action recognition in person centric videos. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8 (2019). https://doi.org/10.1109/AVSS.2019.8909835.

  7. Astriani, M.S., Bahana, R., Kurniawan, A., Yi, L.H.: Threshold-based low power consumption human fall detection for health care and monitoring system. In: 2020 International Conference on Information Management and Technology (ICIMTech), Bandung, Indonesia, pp. 853–857 (2020). https://doi.org/10.1109/ICIMTech50083.2020.9211233.

  8. Wang, X., Jia, K.: Human fall detection algorithm based on YOLOv3. In: 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), Beijing, China, pp. 50–54 (2020). https://doi.org/10.1109/ICIVC50857.2020.9177447.

  9. Kamble, K.P., Sontakke, S.S., Donadkar, H., Poshattiwar, R., Ananth, A.: Fall alert: a novel approach to detect fall using base as a YOLO object detection. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds.) Advanced Machine Learning Technologies and Applications, AMLTA 2020. Advances in Intelligent Systems and Computing, vol. 1141, pp. 15–24. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-3383-9_2

  10. UR Fall Detection Dataset (n.d.). http://fe-nix.univ.rzeszow.pl/~mkepski/ds/uf.html. 28 Sept 2021

  11. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91

  12. Patil, P.W., Biradar, K.M., Dudhane, A., Murala, S.: An end-to-end edge aggregation network for moving object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8149–8158 (2020)

    Google Scholar 

  13. Chaudhary, S., Murala, S.: Deep network for human action recognition using Weber motion. Neurocomputing 367, 207–216 (2019)

    Article  Google Scholar 

  14. Center for Disease Control and Prevention. www.cdc.gov/HomeandRecre-ationalSafety

  15. a/l Kanawathi, J., Mokri, S.S., Ibrahim, N., Hussain, A., Mustafa, M.M.: Motion detection using Horn Schunck algorithm and implementation. In: 2009 International Conference on Electrical Engineering and Informatics, pp. 83–87 (2009). https://doi.org/10.1109/ICEEI.2009.5254812.

  16. Chaudhary, S., Murala, S.: Depth-based end-to-end deep network for human action recognition. IET Comput. Vis. 13(1), 15–22 (2019)

    Article  Google Scholar 

  17. Ahad, M., Rahman, A., Jie, T., Kim, H., Ishikawa, S.: Motion history image: Its variants and applications. Mach. Vis. Appl. 23, 255–281 (2010). https://doi.org/10.1007/s00138-010-0298-4

    Article  Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014, ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23

  19. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016). https://doi.org/10.1109/TPAMI.2015.2437384

  20. Girshick, R.: Fast R-CNN (2015)

    Google Scholar 

  21. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2016)

    Google Scholar 

  22. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177

  23. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks (2016).

    Google Scholar 

  24. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision, ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

  25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)

    Google Scholar 

  26. Horn, B., Schunck, B.: Determining optical flow. Artif. Intell. 17, 185–203 (1981). https://doi.org/10.1016/0004-3702(81)90024-2

    Article  Google Scholar 

  27. Chaudhary, S., Murala, S.: TSNet: deep network for human action recognition in hazy videos. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3981–3986 2018. https://doi.org/10.1109/SMC.2018.00675

  28. Núñez-Marcos, A., Azkune, G., Arganda-Carreras, I.: Vision-based fall detection with convolutional neural networks. Wirel. Commun. Mob. Comput. 1–16 (2017). https://doi.org/10.1155/2017/9474806

  29. Badgujar, S., Pillai, A.S.: Fall detection for elderly people using machine learning. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp. 1–4 (2020). https://doi.org/10.1109/ICCCNT49239.2020.9225494

  30. Hambarde, P., Dudhane, A., Patil, P.W., Murala, S., Dhall, A.: Depth estimation from single image and semantic prior. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1441–1445. IEEE (2020)

    Google Scholar 

  31. Hambarde, P., Dudhane, A., Murala, S.: Single image depth estimation using deep adversarial training. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 989–993. IEEE (2019)

    Google Scholar 

  32. Patil, P.W., Dudhane, A.¸ Kulkarni, A., Murala, S., Gonde, A.B., Gupta, S.: An unified recurrent video object segmentation framework for various surveillance environments. IEEE Trans. Image Process. 30, 7889–7902 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Punjab Engineering College, Chandigarh, India

    Amartya Raghav & Sachin Chaudhary

Authors
  1. Amartya Raghav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sachin Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amartya Raghav .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Raghav, A., Chaudhary, S. (2022). Elderly Patient Fall Detection Using Video Surveillance. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-11349-9_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11348-2

  • Online ISBN: 978-3-031-11349-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics