Abstract
Falls are one of the major health hazards among the aging population aged 65 and above, which could potentially result in a significant hindrance to their independent living. With the advances in medical science in the last few decades, the aging population increases every year, and thus, fall detection system at home is increasingly important. This paper presents a new vision-based fall detection technique that is based on human shape variation where only three points are used to represent a person instead of the conventional ellipse or bounding box. Falls are detected by analyzing the shape change of the human silhouette through the features extracted from the three points. Experiment results show that in comparison with the conventional ellipse and bounding box techniques, the proposed three point–based technique increases the fall detection rate without increasing the computational complexity.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- \(H\) :
-
Height of the bounding box
- \(W\) :
-
Width of the bounding box
- \(h_{R1}\) :
-
Height of region \(R1\)
- \(h_{R2}\) :
-
Height of region \(R2\)
- \(h_{R3}\) :
-
Height of region \(R3\)
- \(w_{R1}\) :
-
Width of region \(R1\)
- \(w_{R2}\) :
-
Width of region \(R2\)
- \(w_{R3}\) :
-
Width of region \(R3\)
- \((g_{R1x},\, g_{R1y})\) :
-
Centroid coordinate of region \(R1\)
- \((g_{R2x},\, g_{R2y})\) :
-
Centroid coordinate of region \(R2\)
- \((g_{R3x},\, g_{R3y})\) :
-
Centroid coordinate of region \(R3\)
- \(D1\) :
-
Distance between \(P1\) and \(P2\)
- \(D2\) :
-
Distance between \(P2\) and \(P3\)
- \(\theta _{1}\) :
-
Orientation of the line formed by \(P1\) and \(P2\)
- \(\theta _{2}\) :
-
Orientation of the line formed by \(P2\) and \(P3\)
- \(p\) :
-
Ratio of the distance \(D1\) over \(D2\)
- \(p_{t-1}\) :
-
Ratio of the distance at previous frame
- \(p_{t}\) :
-
Ratio of the distance at current frame
- \(\theta _{r}\) :
-
Reference angle
- \(D_{r}\) :
-
Length reference
- \(\theta _{N1}\) :
-
Orientation of the line formed by \(P1\) and \(P2\) at the 10th frame after a possible fall
- \(\theta _{N2}\) :
-
Orientation of the line formed by \(P2\) and \(P3\) at the 10th frame after a possible fall
- \(\theta _{D1}\) :
-
Orientation difference between \(\theta _{N1}\) and \(\theta _{r}\)
- \(\theta _{D2}\) :
-
Orientation difference between \(\theta _{N2}\) and \(\theta _{r}\)
- \(\mu _{\theta }\) :
-
Mean of the two orientation differences, \(\theta _{D1}\) and \(\theta _{D2}\)
- \(D_\mathrm{diff}\) :
-
Change in sum of the length of the lines after a possible fall
References
Hausdorff, J.M., Rios, D.A., Edelber, H.K.: Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch. Phys. Med. Rehabil. 82(8), 1050–1056 (2001)
Population Statistics in Malaysia. Department of Statistic Malaysia [Online]. Available: http://www.statistics.gov.my (2010)
Hashim, A.: Overview of Malaysia’s Integrated Telehealth Project. The International Medical Journal. [Online]. Available: http://www.eimjm.com/Vol2-No1/Vol2-No1-I4.htm (2003)
World Population Ageing. United Nations, New York [Online]: Available: http://www.un.org/esa/population/publications/WPA2009/WPA2009-report.pdf (2009)
Falls among Older Adults: An Overview. Web-based Injury Statistics Query and Reporting System (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control). [Online]. Available: http://www.cdc.gov/HomeandRecreationalSafety/Falls/adultfalls.html. Accessed 11 March 2011
Willems, J., Debard, G., Bonroy, B., Vanrumste, B., Goedemé, T.: How to detect human fall in video? An overview. In: International Conference on Positioning and Context-Awareness, Antwerp Belgium, May 2009
Nguyen, T.-T., Cho, M.-C., Lee, T.-S.: Automatic fall detection using wearable biomedical signal measurement terminal. In: Proceedings of 31st Annual International Conference of the IEEE EMBS, pp. 5203–5206 (2009)
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)
Cucchiara, R., Prati, A., Vezzani, R.: A multi-camera vision system for fall detection and alarm generation. Expert Syst J 24(5), 334–345 (2007)
Williams, A., Ganesan, D., Hanson, A.: Aging in place: fall detection and localization in a distributed smart camera network. In: Proceedings of the 15th international conference on Multimedia, pp. 892–901 (2007)
Vishwakarma, V., Mandal, C., Sural, S.: Automatic detection of human fall in video. Lect. Notes Comput. Sci. Patt. Recogn. Mach. Intell. 4815, 616–623 (2007)
Tao, J., Turjo, M., Wong, M. F., Wang, M., Tan, Y. P.: Fall incidents detection for intelligent video surveillance. In: Proceedings of IEEE International Conference on Communication and Signal Processing, pp. 1590–1594 (2005)
Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall detection from human shape and motion history using video surveillance. Proc. 21st Int. Conf. AINAW 2, 875–880 (2007)
Chen, Y.T., Lin, Y.C., Fang, W.H.: A hybrid human fall detection scheme. In: Proceedings of 2010 IEEE 17th International Conference on Image Processing, pp. 3485–3488 (2010)
Acknowledgments
This work was supported in part by Telekom Malaysia Research and Development (TM R&D) research grant.
Conflict of interest The authors declare that they have no competing interests.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chua, JL., Chang, Y.C. & Lim, W.K. A simple vision-based fall detection technique for indoor video surveillance. SIViP 9, 623–633 (2015). https://doi.org/10.1007/s11760-013-0493-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-013-0493-7