Detecting falls and poses in image silhouettes

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/178169
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Type: Examensarbete för masterexamen
Master Thesis
Title: Detecting falls and poses in image silhouettes
Authors: Schräder, Niklas
Abstract: About 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.
Keywords: Elektroteknik och elektronik;Grundläggande vetenskaper;Hållbar utveckling;Informations- och kommunikationsteknik;Innovation och entreprenörskap (nyttiggörande);Electrical Engineering, Electronic Engineering, Information Engineering;Basic Sciences;Sustainable Development;Information & Communication Technology;Innovation & Entrepreneurship
Issue Date: 2011
Publisher: Chalmers tekniska högskola / Institutionen för material- och tillverkningsteknik
Chalmers University of Technology / Department of Materials and Manufacturing Technology
Series/Report no.: Examensarbete - Institutionen för tillämpad mekanik, Chalmers tekniska högskola
URI: https://hdl.handle.net/20.500.12380/178169
Collection:Examensarbeten för masterexamen // Master Theses



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