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Activity recognition and segmentation approaches to multimodal lifelog data

Gupta, Rashmi and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2019) Activity recognition and segmentation approaches to multimodal lifelog data. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), 4-6 Sept 2019, Dublin, Ireland. ISBN 978-1-7281-4673-7

Abstract
—Lifelogging is a phenomenon whereby an individual digitally records his/her personal life experiences, for a variety of purposes. Activity recognition and segmentation is fundamental to many of the use cases in lifelogging. However, detecting sufficiently robust user activity boundaries that could be deployed with confidence in a subjective real-world setting remains a challenge. In this paper, we extend our previous work on identifying a better activity recognition and segmentation approach to multimodal lifelog data, primarily through the introduction of automatic thresholding techniques, but also through revising the criteria for selecting the most appropriate size of sliding window when evaluating the proposed algorithms. We use an open and publicly available lifelog test collection over a time period of 27 days with manual annotations and manually groundtruthed activities.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Image segmentation; Metadata; Visualization; Activity recognition; Manuals; Indexing; TV
Subjects:Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 2019 International Conference on Content-Based Multimedia Indexing (CBMI. . IEEE. ISBN 978-1-7281-4673-7
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/CBMI.2019.8877444
Copyright Information:© 2019 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289
ID Code:24673
Deposited On:23 Jun 2020 11:55 by Cathal Gurrin . Last Modified 15 Dec 2021 15:45
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