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Automatically detecting “significant events” on SenseCam

Li, Na and Crane, Martin and Ruskin, Heather J. (2013) Automatically detecting “significant events” on SenseCam. International Journal of Wavelets, Multiresolution and Information Processing, 11 (6). ISSN 0219-6913

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SenseCam is an effective memory-aid device that can automatically record images and other data from the wearer’s whole day. The main issue is that, while SenseCam produces a sizeable collection of images over the time period, the vast quantity of captured data contains a large percentage of routine events, which are of little interest to review. In this article, the aim is to detect “Significant Events” for the wearers.We use several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyse the multiple time series generated by the SenseCam. We show that Detrended Fluctuation Analysis exposes a strong long-range correlation relationship in SenseCam collections. Maximum Overlap Discrete Wavelet Transform (MODWT) was used to calculate equal-time Correlation Matrices over different time scales and then explore the granularity of the largest eigenvalue and changes of the ratio of the sub dominant eigenvalue spectrum dynamics over sliding time windows. By examination of the eigenspectrum, we show that these approaches enable detection of major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. We suggest that some wavelet scales (e.g. 8 minutes-16 minutes) have the potential to identify distinct events or activities.

Item Type:Article (Published)
Subjects:Computer Science > Lifelog
Computer Science > Image processing
Computer Science > Algorithms
Mathematics > Mathematical physics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:World Scientific
Official URL:
Copyright Information:© 2016 World Scientific
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21305
Deposited On:29 Jul 2016 14:41 by Na Li. Last Modified 24 Feb 2017 12:48

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