Skip to main content
Book cover

UK Workshop on Computational Intelligence

UKCI 2017: Advances in Computational Intelligence Systems pp 276–289Cite as

A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 650)

Abstract

Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications.

Keywords

  • Fall detection
  • Local feature extraction
  • HOG
  • HMG
  • HOF
  • MBH
  • Artificial neural network

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-66939-7_24
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-66939-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

References

  1. Litvak, D., Zigel, Y., Gannot, I.: Fall detection of elderly through floor vibrations and sound. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4632–4635 (2008)

    Google Scholar 

  2. Zhu, L., Zhou, P., Pan, A., Guo, J., Sun, W., Wang, L., Chen, X., Liu, Z.: A survey of fall detection algorithm for elderly health monitoring. In: 2015 IEEE Fifth International Conference on Big Data and Cloud Computing, pp. 270–274 (2015)

    Google Scholar 

  3. Mohamed, O., Choi, H.J., Iraqi, Y.: Fall detection systems for elderly care: a survey. In: 2014 6th International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–4 (2014)

    Google Scholar 

  4. AlZubi, H.S., Gerrard-Longworth, S., Al-Nuaimy, W., Goulermas, Y., Preece, S.: Human activity classification using a single accelerometer. In: 2014 14th UK Workshop on Computational Intelligence (UKCI), pp. 1–6. IEEE (2014)

    Google Scholar 

  5. Zhang, Z., Conly, C., Athitsos, V.: A survey on vision-based fall detection. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 46. ACM (2015)

    Google Scholar 

  6. Mirmahboub, B., Samavi, S., Karimi, N., Shirani, S.: Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Trans. Biomed. Eng. 60(2), 427–436 (2013)

    CrossRef  Google Scholar 

  7. Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. Circuits Syst. Video Technol. 21(5), 611–622 (2011)

    CrossRef  Google Scholar 

  8. Merrouche, F., Baha, N.: Depth camera based fall detection using human shape and movement. In: IEEE International Conference on Signal and Image Processing (ICSIP), pp. 586–590. IEEE (2016)

    Google Scholar 

  9. Duta, I.C., Uijlings, J.R.R., Nguyen, T.A., Aizawa, K., Hauptmann, A.G., Ionescu, B., Sebe, N.: Histograms of motion gradients for real-time video classification. In: 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2016)

    Google Scholar 

  10. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  11. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR 2008, pp. 1–8, June 2008

    Google Scholar 

  12. Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). doi:10.1007/11744047_33

    CrossRef  Google Scholar 

  13. Popescu, M., Mahnot, A.: Acoustic fall detection using one-class classifiers. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3505–3508 (2009)

    Google Scholar 

  14. Delahoz, Y.S., Labrador, M.A.: Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10), 19806–19842 (2014)

    CrossRef  Google Scholar 

  15. Hwang, J.Y., Kang, J.M., Jang, Y.W., Kim, H.C.: Development of novel algorithm and real-time monitoring ambulatory system using bluetooth module for fall detection in the elderly. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004. IEMBS 2004, vol. 1, pp. 2204–2207. IEEE (2004)

    Google Scholar 

  16. Luštrek, M., Kaluža, B.: Fall detection and activity recognition with machine learning. Informatica 33(2), 205–212 (2009)

    Google Scholar 

  17. Kerdegari, H., Samsudin, K., Ramli, A.R., Mokaram, S.: Evaluation of fall detection classification approaches. In: 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS), vol. 1, pp. 131–136. IEEE (2012)

    Google Scholar 

  18. Degen, T., Jaeckel, H., Rufer, M., Wyss, S.: Speedy: a fall detector in a wrist watch. In: ISWC, pp. 184–189 (2003)

    Google Scholar 

  19. Planinc, R., Kampel, M.: Introducing the use of depth data for fall detection. Personal Ubiquit. Comput. 17(6), 1063–1072 (2013)

    CrossRef  Google Scholar 

  20. Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J.: Fall detection from depth map video sequences. In: Toward useful Services for Elderly and People with Disabilities, pp. 121–128 (2011)

    Google Scholar 

  21. Albawendi, S., Appiah, K., Powell, H., Lotfi, A.: Video based fall detection with enhanced motion history images. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 29. ACM (2016)

    Google Scholar 

  22. Uijlings, J., Duta, I.C., Sangineto, E., Sebe, N.: Video classification with densely extracted hog/hof/mbh features: an evaluation of the accuracy/computational efficiency trade-off. Int. J. Multimed. Inf. Retr. 4(1), 33–44 (2015)

    CrossRef  Google Scholar 

  23. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision (IJCV) 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  24. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, vol. 1, pp. I-511-I-518 (2001)

    Google Scholar 

  25. Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)

    CrossRef  Google Scholar 

  26. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence - vol. 2, IJCAI 1981, pp. 674–679. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1981)

    Google Scholar 

  27. Wang, S., Yang, K.J.: An image scaling algorithm based on bilinear interpolation with VC++. Tech. Autom. Appl. 27(7), 44–45 (2008)

    Google Scholar 

  28. Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2911–2918. IEEE (2012)

    Google Scholar 

  29. Jolliffe, I.: Principal Component Analysis. Wiley, Hoboken (2002)

    MATH  Google Scholar 

  30. Haykin, S.S.: Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River (2009)

    Google Scholar 

  31. Liu, Y., Jing, W., Xu, L.: Parallelizing backpropagation neural network using mapreduce and cascading model. Comput. Intell. Neurosci. 2016, 2842780:1–2842780:11 (2016)

    Google Scholar 

  32. De Villiers, J., Barnard, E.: Backpropagation neural nets with one and two hidden layers. IEEE Trans. Neural Netw. 4(1), 136–141 (1993)

    CrossRef  Google Scholar 

  33. Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)

    CrossRef  Google Scholar 

  34. Jensen, R., Shen, Q.: Are more features better? A response to attributes reduction using fuzzy rough sets. IEEE Trans. Fuzzy Syst. 17(6), 1456–1458 (2009)

    CrossRef  Google Scholar 

  35. Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8. IEEE (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

    Ryan Cameron, Zheming Zuo, Graham Sexton & Longzhi Yang

Authors
  1. Ryan Cameron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheming Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graham Sexton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Longzhi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Longzhi Yang .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen Shi, Fujian, China

    Fei Chao

  2. Cardiff University, Cardiff, United Kingdom

    Steven Schockaert

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Cameron, R., Zuo, Z., Sexton, G., Yang, L. (2018). A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66939-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66938-0

  • Online ISBN: 978-3-319-66939-7

  • eBook Packages: EngineeringEngineering (R0)