Detecting falls and poses in image silhouettes

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dc.contributor.authorSchräder, Niklas
dc.contributor.departmentChalmers tekniska högskola / Institutionen för material- och tillverkningstekniksv
dc.contributor.departmentChalmers University of Technology / Department of Materials and Manufacturing Technologyen
dc.date.accessioned2019-07-03T13:10:30Z
dc.date.available2019-07-03T13:10:30Z
dc.date.issued2011
dc.description.abstractAbout one third of all people aged 65 and above will accidentally fall during one year. A fall can have severe consequences,such as fractures, and a fallen person might need assistance at getting up again. A lot of research has been dedicated into the development of automatic fall detection methods during the recent years. These automatic methods are created to detect falls so an alarm can be raised and help can come. In this thesis, a part of a fall detection system for a household robot aimed at helping the elderly is developed. The system is able to classify human pose from a silhouette in an image. By associating the pose “lying down” with a fallen person, the system can be used for fall detection. The algorithm is based on an image analysis feature called shape contexts. These shape contexts describe distributions of edge points by binning them into polar histograms. Altough the dataset used for training contains falls in many difficult angles, the algorithm classifies falls correctly for 97 % of a set of unseen images.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/178169
dc.language.isoeng
dc.relation.ispartofseriesExamensarbete - Institutionen för tillämpad mekanik, Chalmers tekniska högskola
dc.setspec.uppsokTechnology
dc.subjectElektroteknik och elektronik
dc.subjectGrundläggande vetenskaper
dc.subjectHållbar utveckling
dc.subjectInformations- och kommunikationsteknik
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectElectrical Engineering, Electronic Engineering, Information Engineering
dc.subjectBasic Sciences
dc.subjectSustainable Development
dc.subjectInformation & Communication Technology
dc.subjectInnovation & Entrepreneurship
dc.titleDetecting falls and poses in image silhouettes
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeApplied mechanics (MPAME), MSc
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