Skip to main content
SpringerLink
Log in

Human fall detection using normalized shape aspect ratio

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In video surveillance, automatic human fall detection is important to protect vulnerable groups such as the elderly. When the camera layout varies, the shape aspect ratio (SAR) of a human body may change substantially. In order to rectify these changes, in this paper, we propose an automatic human fall detection method using the normalized shape aspect ratio (NSAR). A calibration process and bicubic interpolation are implemented to generate the NSAR table for each camera. Compared with some representative fall detection methods using the SAR, the proposed method integrates the NSAR with the moving speed and direction information to robustly detect human fall, as well as being able to detect falls toward eight different directions for multiple humans. Moreover, while most of the existing fall detection methods were designed only for indoor environment, experimental results demonstrate that this newly proposed method can effectively detect human fall in both indoor and outdoor environments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Alhimale L, Zedan H, Al-Bayatti A (2014) The implementation of an intelligent and video-based fall detection system using a neural network. Appl Soft Comput 18:59–69

    Article  Google Scholar 

  2. Alwan M, Rajendran PJ, Kell S, et al (2006) A Smart and Passive Floor-Vibration Based Fall Detector for Elderly. In: 2nd International Conference on Information & Communication Technologies, Damascus, pp. 1003–1007

  3. Augustyniak P, Smoleń M, Mikrut Z, Kańtoch E (2014) Seamless tracing of human behavior using complementary wearable and house-embedded sensors. Sensors 14:7831–7856

    Article  Google Scholar 

  4. Bhattacharyya A (1942) On a measure of divergence between two statistical populations defined by their probability distributions. Bull Calcutta Math Soc 35:99–109

    MathSciNet  MATH  Google Scholar 

  5. Chen M-C (2016) A video surveillance system designed to detect multiple falls. Adv Mech Eng 8(4):1–11

    Google Scholar 

  6. Chen M-C, Liu Y-M (2013) An indoor video surveillance system with intelligent fall detection capability. Math Probl Eng:1–8

  7. Chu J, Guo Z, Leng L (2018) Object Detection Based on Multi-Layer Convolution Feature Fusion and Online Hard Example Mining. IEEE Access PP:1–1

  8. Chua J-L, Chang YC, Lim WK (2015) A simple vision-based fall detection technique for indoor video surveillance. SIViP 9:623–633

    Article  Google Scholar 

  9. Cola G, Avvenuti M, Vecchio A et al (2015) An on-node processing approach for anomaly detection in gait. IEEE Sensors J 15:6640–6649

    Article  Google Scholar 

  10. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 142–149

  11. Feng P, Yu M, Naqvi SM, Chambers JA (2014) Deep learning for posture analysis in fall detection. In: 19th International Conference on Digital Signal Processing, 12–17

  12. Feng W, Liu R, Zhu M (2014) Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera. SIViP 8:1129–1138

    Article  Google Scholar 

  13. Hazelhoff L, Han J, de With PHN (2008) Video-Based Fall Detection in the Home Using Principal Component Analysis. Advanced Concepts for Intelligent Vision Systems. 298–309

  14. Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. Biomed Eng Online 12:66

    Article  Google Scholar 

  15. Ji T, Turjo M, Wong M-F, et al (2005) Fall Incidents Detection for Intelligent Video Surveillance. In: 5th International Conference on Information Communications & Signal Processing. 1590–1594

  16. Lai C-F, Chen M, Pan J-S et al (2014) A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction. IEEE J Biomed Health Inform 18:457–466

    Article  Google Scholar 

  17. Li L, Huang W, Gu IYH, Tian Q (2003) Foreground Object Detection from Videos Containing Complex Background. In: Proceedings of the 11th ACM International Conference on Multimedia. 2–10

  18. Liu H, Zuo C (2012) An improved algorithm of automatic fall detection. AASRI Procedia 1:353–358

    Article  Google Scholar 

  19. Liu Y, Nie L, Han L, et al. (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. 1617–1623

  20. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  21. Medrano C, Plaza I, Igual R et al (2016) The effect of personalization on smartphone-based fall detectors. Sensors 16(1):117

    Article  Google Scholar 

  22. Mirmahboub B, Samavi S, Karimi N, Shirani S (2013) Automatic monocular system for human fall detection based on variations in Silhouette area. IEEE Trans Biomed Eng 60:427–436

    Article  Google Scholar 

  23. Mubashir M, Shao L, Seed L (2013) A survey on fall detection: principles and approaches. Neurocomputing 100:144–152

    Article  Google Scholar 

  24. Núñez-Marcos A, Azkune G, Arganda-Carreras I (2017) Vision-Based Fall Detection with Convolutional Neural Networks. Wireless Communications and Mobile Computing Vol. 2017, Article ID 9474806, 16

  25. Rhuma A, Yu M, Chambers J (2013) Posture Recognition Based Fall Detection System. Lecture Notes on Software Engineering. 350–355

  26. Rimminen H, Lindstrom J, Linnavuo M, Sepponen R (2010) Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Trans Inf Technol Biomed 14:1475–1476

    Article  Google Scholar 

  27. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2007) Fall Detection from Human Shape and Motion History Using Video Surveillance. In: 21st International Conference on Advanced Information Networking and Applications Workshops. 875–880

  28. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21:611–622

    Article  Google Scholar 

  29. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2013) 3D head tracking for fall detection using a single calibrated camera. Image Vis Comput 31:246–254

    Article  Google Scholar 

  30. Tabar AM, Keshavarz A, Aghajan H (2006) Smart home care network using sensor fusion and distributed vision-based reasoning. In: 4th ACM International Workshop on Video Surveillance and Sensor Networks Pages 145–154

  31. Tra K, Pham TV (2013) Human fall detection based on adaptive background mixture model and HMM. international conference on advanced technologies for. Communications:95–100

  32. Xiao H-E, Wang Z-L, Li Q, Wang N-M (2013) Gaussian mixture model for background based automatic fall detection. Institution of Engineering and Technology, 234–237

  33. Xu H, Das A, Saenko K (2017) R-C3D: Region Convolutional 3D Network for Temporal Activity Detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). 5794–5803

  34. Yu M, Naqvi SM, Rhuma A, Chambers J (2012) One class boundary method classifiers for application in a video-based fall detection system. IET Comput Vis 90–100

  35. Yu M, Gong L, Kollias S (2017) Computer vision based fall detection by a convolutional neural network. In: proceedings of the 19th ACM international conference on multimodal. Interaction:416–420

  36. Zhu H, Vial R, Lu S (2017) TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5813–5821

  37. Zhuang X, Huang J, Potamianos G, Hasegawa-Johnson M (2009) Acoustic fall detection using Gaussian mixture models and GMM supervectors. In: IEEE International Conference on Acoustics, Speech and Signal Processing. 69–72

  38. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th IEEE International Conference on Pattern Recognition. 2; 28–31

Download references

Acknowledgements

This research was supported by National Natural Science Foundation of China under Grant 61463032, 61762061, 61703198, the Natural Science Foundation of Jiangxi Province, China under Grant 20161ACB20004 and 2018ACB21014, and the Open Fund of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, under Grant 20180109.

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang University, Nanchang, 330031, China

    Weidong Min, Song Zou & Jing Li

  2. School of Software Engineering, Nanchang University, Nanchang, 330029, China

    Weidong Min

  3. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Jing Li

Authors
  1. Weidong Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Song Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Li.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Min, W., Zou, S. & Li, J. Human fall detection using normalized shape aspect ratio. Multimed Tools Appl 78, 14331–14353 (2019). https://doi.org/10.1007/s11042-018-6794-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6794-7

Keywords

Search

Navigation