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Automatic fall detection from real-time video streams in indoor environments

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dc.date.accessioned 2018-10-03T12:58:11Z
dc.date.available 2018-10-03T12:58:11Z
dc.date.issued 2018-10-03
dc.identifier.uri http://hdl.handle.net/123456789/21112
dc.title Automatic fall detection from real-time video streams in indoor environments en
ethesis.discipline Computer science en
ethesis.discipline Tietojenkäsittelytiede fi
ethesis.discipline Datavetenskap sv
ethesis.discipline.URI http://data.hulib.helsinki.fi/id/1dcabbeb-f422-4eec-aaff-bb11d7501349
ethesis.department.URI http://data.hulib.helsinki.fi/id/225405e8-3362-4197-a7fd-6e7b79e52d19
ethesis.department Institutionen för datavetenskap sv
ethesis.department Department of Computer Science en
ethesis.department Tietojenkäsittelytieteen laitos fi
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca
ethesis.university.URI http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97
ethesis.university Helsingin yliopisto fi
ethesis.university University of Helsinki en
ethesis.university Helsingfors universitet sv
dct.creator Gafurova, Lina
dct.issued 2018
dct.language.ISO639-2 eng
dct.abstract Automatic fall detection is a very important challenge in the public health care domain. The problem primarily concerns the growing population of the elderly, who are at considerably higher risk of falling down. Moreover, the fall downs for the elderly may result in serious injuries or even death. In this work we propose a solution for fall detection based on machine learning, which can be integrated into a monitoring system as a detector of fall downs in image sequences. Our approach is solely camera-based and is intended for indoor environments. For successful detection of fall downs, we utilize the combination of the human shape variation determined with the help of the approximated ellipse and the motion history. The feature vectors that we build are computed for sliding time windows of the input images and are fed to a Support Vector Machine for accurate classification. The decision for the whole set of images is based on additional rules, which help us restrict the sensitivity of the method. To fairly evaluate our fall detector, we conducted extensive experiments on a wide range of normal activities, which we used to oppose the fall downs. Reliable recognition rates suggest the effectiveness of our algorithm and motivate us for improvement. en
dct.language en
ethesis.isPublicationLicenseAccepted false
ethesis.language.URI http://data.hulib.helsinki.fi/id/languages/eng
ethesis.language English en
ethesis.language engelska sv
ethesis.language englanti fi
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype.URI http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis
dct.identifier.ethesis E-thesisID:1d1b0a46-9840-4373-bb3f-16b085a24745
dct.identifier.urn URN:NBN:fi-fe201804208645
dc.type.dcmitype Text

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