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Automatically detecting “significant events” on SenseCam
Li, Na; Crane, Martin; Ruskin, Heather J.
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.
Keyword(s): Lifelog; Image processing; Algorithms; Mathematical physics
Publication Date:
2013
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Li, Na, Crane, Martin ORCID: 0000-0001-7598-3126 <https://orcid.org/0000-0001-7598-3126> and Ruskin, Heather J. ORCID: 0000-0001-7101-2242 <https://orcid.org/0000-0001-7101-2242> (2013) Automatically detecting “significant events” on SenseCam. International Journal of Wavelets, Multiresolution and Information Processing, 11 (6). ISSN 0219-6913
Publisher(s): World Scientific
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/21305/1/ws-ijwmip.pdf,
http://www.worldscientific.com/doi/abs/10.1142/S0219691313500501
First Indexed: 2016-07-30 05:05:37 Last Updated: 2019-02-09 06:17:12