Abstract
Changing clothes is a basic activity of daily living (ADL) which may be used as a measurement of the functional status of e.g. an elderly person, or a person with certain disabilities. In this paper we propose a methodology for the detection of when a human has changed clothes. Our non-contact unobtrusive monitoring system is built upon the Microsoft Kinect depth camera. It uses the OpenNI SDK to detect a human skeleton and extract the upper and lower clothes’ visual features. Color, texture and edge descriptors are then extracted and fused. We evaluate our system on a publicly available set of real recordings for several users and under various illumination conditions. Our results show that our system is able to successfully detect when a user changes clothes, thus to assess the quality of the corresponding ADL.
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Notes
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“positive” refers to clothes change detection, therefore “negative” means “no clothes change”.
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Acknowledgements
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 288532. For more details, please see http://www.usefil.eu.
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Sgouropoulos, D., Giannakopoulos, T., Siantikos, G., Spyrou, E., Perantonis, S. (2015). Detection of Clothes Change Fusing Color, Texture, Edge and Depth Information. In: Obaidat, M., Holzinger, A., Filipe, J. (eds) E-Business and Telecommunications. ICETE 2014. Communications in Computer and Information Science, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-319-25915-4_20
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