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Title: Camera-based fall detection on real world data
Authors: Debard, Glen
Karsmakers, Peter
Deschodt, Mieke
Vlaeyen, Ellen
Dejaeger, Eddy
Milisen, Koen
Goedemé, Toon
Vanrumste, Bart
Tuytelaars, Tinne
Issue Date: 2012
Publisher: Springer Berlin Heidelberg
Series Title: Lecture Notes in Computer Science vol:7474
Host Document: Outdoor and large-scale real-world scene analysis pages:356-375
Conference: International Workshop on Theoretical Foundations of Computer Vision edition:15 location:Dagstuhl Castle, Germany date:26 June - 1 July 2011
Abstract: Several new algorithms for camera-based fall detection have been proposed in the literature recently, with the aim to monitor older people at home so nurses or family members can be warned in case of a fall incident. However, these algorithms are evaluated almost exclusively on data captured in controlled environments, under optimal conditions (simple scenes, perfect illumination and setup of cameras), and with falls simulated by actors.

In contrast, we collected a dataset based on real life data, recorded at the place of residence of four older persons over several months. We showed that this poses a significantly harder challenge than the datasets used earlier. The image quality is typically low. Falls are rare and vary a lot both in speed and nature. We investigated the variation in environment parameters and context during the fall incidents. We found that various complicating factors, such as moving furniture or the use of walking aids, are very common yet almost unaddressed in the literature. Under such circumstances and given the large variability of the data in combination with the limited number of examples available to train the system, we posit that simple yet robust methods incorporating, where available, domain knowledge (e.g. the fact that the background is static or that a fall usually involves a downward motion) seem to be most promising. Based on these observations, we propose a new fall detection system. It is based on background subtraction and simple measures extracted from the dominant foreground object such as aspect ratio, fall angle and head speed. We discuss the results obtained, with special emphasis on particular difficulties encountered under real world circumstances.
Description: Debard G., Karsmakers P., Deschodt M., Vlaeyen E., Dejaeger E., Milisen K., Goedemé T., Vanrumste B., Tuytelaars T., ''Camera-based fall detection on real world data'', Lecture notes in computer science: outdoor and large-scale real-world scene analysis, vol. 7474, pp. 356-375, Dellaert F., Frahm J.-M., Pollefeys M., Leal-Taixé L. and Rosenhahn B. eds., 2012 (15th international workshop on theoretical foundations of computer vision, June 26 - July 1, 2011, Dagstuhl Castle, Germany).
ISBN: 978-3-642-34091-8
978-3-642-34090-1
Publication status: published
KU Leuven publication type: IHb
Appears in Collections:ESAT Electrical Engineering TC @ Thomas More Kempen
ESAT Electrical Engineering TC @ Thomas More Mechelen
Technologiecluster ESAT Elektrotechnische Engineering
Faculteit Industrieel Ingenieur - KHLim
Gerontology and Geriatrics
Departement Gezondheidszorg & Technologie - KHLeuven
Centre for Health Services and Nursing Research
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
ESAT - PSI, Processing Speech and Images

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