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Using visual lifelogs to automatically characterise everyday activities

Wang, Peng and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2013) Using visual lifelogs to automatically characterise everyday activities. Information Sciences, 230 . pp. 147-161. ISSN 0020-0255

Abstract
Visual lifelogging is the term used to describe recording our everyday lives using wearable cameras, for applications which are personal to us and do not involve sharing our recorded data. Current applications of visual lifelogging are built around remembrance or searching for specific events from the past. The purpose of the work reported here is to extend this to allow us to characterise and measure the occurrence of everyday activities of the wearer and in so doing to gain insights into the wearer's everyday behaviour. The methods we use are to capture everyday activities using a wearable camera called SenseCam, and to use an algorithm we have developed which indexes lifelog images by the occurrence of basic semantic concepts. We then use data reduction techniques to automatically generate a profile of the wearer's everyday behaviour and activities. Our algorithm has been evaluated on a large set of concepts investigated from 13 users in a user experiment, and for a group of 16 popular everyday activities we achieve an average F-score of 0.90. Our conclusions are that the the technique we have presented for unobtrusively and ambiently characterising everyday behaviour and activities across individuals is of sufficient accuracy to be usable in a range of applications.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Human behaviour
Subjects:Computer Science > Lifelog
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Publisher:Elsevier
Official URL:http://dx.doi.org/10.1016/j.ins.2012.12.028
Copyright Information:© 2013 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, China Scholarship Council
ID Code:17708
Deposited On:18 Feb 2013 09:38 by Alan Smeaton . Last Modified 31 Oct 2018 12:43
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