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Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Shewell, Colin, Medina-Quero, J., Espinilla, M., Nugent, Chris, Donnelly, Mark and Wang, Haiying / HY (2016) Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor. International Journal of Communication Systems . n/a-n/a. [Journal article]

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URL: http://dx.doi.org/10.1002/dac.3223

DOI: 10.1002/dac.3223

Abstract

This paper presents a comparison between algorithms (Oriented FAST and Rotated BRIEF (ORB) and Aruco) for the detection of fiducial markers placed throughout a smart environment. A series of activities of daily living (ADL) were conducted while monitoring a first-person perspective of the situation; this was achieved through the usage of the Google Glass platform. Fiducial markers were employed, as a means to assist with the detection of specific objects of interest, within the environment. Each marker was assigned unique Identification (ID) and was used to identify the object. Three activities were performed by a participant within the environment. On subsequent trials of the solution, lighting conditions were modified to assess fiducial marker detection rates on a frame-by-frame basis. This paper presents the results from this investigation, detailing performance measure for each object detected under various lighting conditions, motion blur and distance from the objects. An intelligent system was developed to specifically consider distance estimation in order to aid with the filtering out of false interactions. A linear filtering method was applied along with a fuzzy membership function to estimate the degree of user interaction, which assists in removing false positives generated by the occupant. The intelligent system returns an average precision, recall and an F-Measure of 0.99, 0.62 and 0.49, respectively.

Item Type:Journal article
Keywords:Aruco, fiducial, localisation, machine-vision, ORB, wearable
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:36107
Deposited By: Dr Mark Donnelly
Deposited On:28 Oct 2016 10:42
Last Modified:28 Oct 2016 10:42

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