Abstract
Falling is one of the major risks for elderly people, kids and people with disabilities. The situation worsens when the victim suffers from serious injuries and is unable to get help on time. In this paper, we propose a method to detect a fall in real-time. The proposed detection method consists of three stages: Video analysis, Body Recognition and Trigger Alert. In this recognition system, human detection algorithms using OpenCV have been implemented. The application accuracy has been tested under different lighting settings and in different environment settings.
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Yang, L., Ren, Y., Zhang, W.: 3D depth image analysis for indoor fall detection of elderly people. Digit. Commun. Netw. 2(1), 24–34 (2016)
Alaliyat, S.: Video - based fall detection in elderly’s houses (2008)
Gaikwad, K., Patil, D.: Human activity detection and recognition algorithm from video surveillances. Int. J. Technol. Explor. Learn. 2(5), 198–202 (2013)
Salmi, K.: Improving safety for home care patients with a low-cost computer vision solution (2016). https://doi.org/10.13140/RG.2.2.35833.06242
Schrader, N.: Detecting falls and poses in image silhouettes (2011)
Skolan, V., et al.: A method for real-time detection of human fall from video (2012)
Van Tuan, P., Le Uyen Thuc, H.: An effective video based system for human fall detection. Int. J. Adv. Res. Comput. Eng. Technol. 3(8), 2820–2826 (2014)
Le2i.cnrs.fr.: Fall detection dataset - Le2i - laboratoire electronique, informatique et image (2018)
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Mourey, J., Sehat Niaki, A., Kaplish, P., Gupta, R. (2020). Human Body Fall Recognition System. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_31
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DOI: https://doi.org/10.1007/978-3-030-54407-2_31
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