Multimedia Tools and Applications

, Volume 75, Issue 19, pp 11603–11613 | Cite as

Human fall detection in surveillance video based on PCANet

  • Shengke Wang
  • Long Chen
  • Zixi Zhou
  • Xin Sun
  • Junyu Dong
Article

Abstract

Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.

Keywords

Fall detection PCANet Behavior analysis Patient monitoring Visual surveillance 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) Grants 61301241, 61401413, 61403353 and 61271405.

References

  1. 1.
    Alwan M et al. (2006) A smart and passive floor-vibration based fall detector for elderly. in Information and Communication Technologies, 2006. ICTTA’06. 2nd. IEEE.Google Scholar
  2. 2.
    Auvinet E et al (2010) Multiple cameras fall dataset. DIRO-Université de Montréal, Tech. Rep, 1350.Google Scholar
  3. 3.
    Barnich O, Van Droogenbroeck M (2011) ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Machine Intell 35(8):1798–1828CrossRefGoogle Scholar
  5. 5.
    Bourke AK, Lyons GM (2008) A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys 30(1):84–90CrossRefGoogle Scholar
  6. 6.
    Cetin A (2013) Ambient assisted smart home design using vibration and PIR sensors. in Signal Processing and Communications Applications Conference (SIU), 2013 21st. IEEEGoogle Scholar
  7. 7.
    Chan T-H et al. (2014) PCANet: a simple deep learning baseline for image classification? arXiv preprint arXiv:1404.3606Google Scholar
  8. 8.
    Chen Y-T, Lin Y-C, Fang W-H (2010) A hybrid human fall detection scheme. in Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEEGoogle Scholar
  9. 9.
    Chua J-L, Chang YC, Lim WK (2013) A simple vision-based fall detection technique for indoor video surveillance. Signal, Image and Video Processing: p. 1–11.Google Scholar
  10. 10.
    Hausdorff JM, Rios DA, Edelberg HK (2001) Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil 82(8):1050–1056CrossRefGoogle Scholar
  11. 11.
    Hinton G, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Kangas M et al (2008) Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28(2):285–291CrossRefGoogle Scholar
  13. 13.
    Keskin F, Töreyin BU, Çetin AE (2013) Fall detection using single-tree complex wavelet transform. Pattern Recogn Lett 34(15):1945–1952CrossRefGoogle Scholar
  14. 14.
    Mubashir M, Shao L, Seed L (2013) A survey on fall detection: principles and approaches. Neurocomputing 100:144–152CrossRefGoogle Scholar
  15. 15.
    Nguyen T-T, Cho M-C, Lee T-S (2009) Automatic fall detection using wearable biomedical signal measurement terminal. in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. IEEEGoogle Scholar
  16. 16.
    Rougier C et al. (2007) Fall detection from human shape and motion history using video surveillance. in Advanced Information Networking and Applications Workshops, 2007, AINAW’07. 21st International Conference on. IEEE.Google Scholar
  17. 17.
    Rougier C et al (2011) Robust video surveillance for fall detection based on human shape deformation. C Syst Video Technology, IEEE Transactions on 21(5):611–622CrossRefGoogle Scholar
  18. 18.
    Tao J et al. (2005) Fall incidents detection for intelligent video surveillance. in Information, Communications and Signal Processing, 2005 Fifth International Conference on. IEEEGoogle Scholar
  19. 19.
    Vishwakarma V, Mandal C, Sural S (2007) Automatic detection of human fall in video, in Pattern Recognition and Machine Intelligence. Springer. p. 616–623.Google Scholar
  20. 20.
    Wild D, Nayak U, Isaacs B (1981) How dangerous are falls in old people at home? Br Med J (Clin Res Ed) 282(6260):266CrossRefGoogle Scholar
  21. 21.
    Williams A, Ganesan D, Hanson A (2007) Aging in place: fall detection and localization in a distributed smart camera network. in Proceedings of the 15th international conference on Multimedia. ACMGoogle Scholar
  22. 22.
    Yu X (2008) Approaches and principles of fall detection for elderly and patient. in e-health Networking, Applications and Services, 2008. HealthCom 2008. 10th International Conference on. IEEEGoogle Scholar
  23. 23.
    Zambanini S, Machajdik J, Kampel M (2010) Detecting falls at homes using a network of low-resolution cameras. in Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on. IEEE.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Shengke Wang
    • 1
  • Long Chen
    • 1
  • Zixi Zhou
    • 1
  • Xin Sun
    • 1
  • Junyu Dong
    • 1
  1. 1.Ocean University of ChinaQingdaoChina

Personalised recommendations