Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor. (6th October 2016)

• Record Type: Journal Article
• Authors: Shewell, C.; Medina‐Quero, J.; Espinilla, M.; Nugent, C.; Donnelly, M.; Wang, H.
• Other Names: Ning Huansheng guestEditor.; El Baz Didier guestEditor.; Yang Laurence T. guestEditor.; Wang Rui guestEditor.
• Is Part Of: International journal of communication systems. Volume 30:Number 5(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-10-06
• Subjects: Aruco -- fiducial -- localisation -- machine‐vision -- ORB -- wearable; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.3223
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: 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. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : A comparison of ORB and Aruco algorithms was carried out with the goal of detecting fiducial markers. Results show Aruco offering superior general detection performance with ORB outperforming Aruco in extreme lighting conditions. Offering a non‐intrusive method of detecting occupant‐object interaction and localisation, minimising the cost of hardware, implementation and maintenance costs. The intelligent system for detecting inhabitant‐object interaction (ISDII) system provides a low computational method of determining object interaction and isolating false positives. Returning average precision, recall and F‐Measure of 0.99, 0.62 and 0.49, respectively.