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Eyes of Things

VISILAB, University of Castilla-La Mancha, E.T.S.I.Industriales, Avda Camilo Jose Cela s/n, Ciudad Real 13071, Spain
Movidius, 1st Floor, O’Connell Bridge House, D’Olier Street, Dublin 2, Ireland
DFKI, Augmented Vision Research Group, Tripstaddterstr. 122, 67663 Kaiserslautern, Germany
Awaiba, Madeira Tecnopolo, 9020-105 Funchal, Portugal
nViso SA, PSE-D, Site EPFL, CH-1015 Lausanne, Switzerland
THALES Communications & Security, 4 Avenue des Louvresses, 92230 Gennevilliers, France
Evercam, 6-7 Granby Row, Dublin 1, D01 FW20, Ireland
Fluxguide, Burggasse 7-9/9, 1070 Vienna, Austria
Author to whom correspondence should be addressed.
Academic Editors: Luca Roselli, Federico Alimenti and Stefania Bonafoni
Sensors 2017, 17(5), 1173;
Received: 30 March 2017 / Revised: 4 May 2017 / Accepted: 17 May 2017 / Published: 21 May 2017
(This article belongs to the Special Issue New Generation Sensors Enabling and Fostering IoT)
Embedded systems control and monitor a great deal of our reality. While some “classic” features are intrinsically necessary, such as low power consumption, rugged operating ranges, fast response and low cost, these systems have evolved in the last few years to emphasize connectivity functions, thus contributing to the Internet of Things paradigm. A myriad of sensing/computing devices are being attached to everyday objects, each able to send and receive data and to act as a unique node in the Internet. Apart from the obvious necessity to process at least some data at the edge (to increase security and reduce power consumption and latency), a major breakthrough will arguably come when such devices are endowed with some level of autonomous “intelligence”. Intelligent computing aims to solve problems for which no efficient exact algorithm can exist or for which we cannot conceive an exact algorithm. Central to such intelligence is Computer Vision (CV), i.e., extracting meaning from images and video. While not everything needs CV, visual information is the richest source of information about the real world: people, places and things. The possibilities of embedded CV are endless if we consider new applications and technologies, such as deep learning, drones, home robotics, intelligent surveillance, intelligent toys, wearable cameras, etc. This paper describes the Eyes of Things (EoT) platform, a versatile computer vision platform tackling those challenges and opportunities. View Full-Text
Keywords: embedded computer vision; eyes of things; Internet of Things; computer vision embedded computer vision; eyes of things; Internet of Things; computer vision
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MDPI and ACS Style

Deniz, O.; Vallez, N.; Espinosa-Aranda, J.L.; Rico-Saavedra, J.M.; Parra-Patino, J.; Bueno, G.; Moloney, D.; Dehghani, A.; Dunne, A.; Pagani, A.; Krauss, S.; Reiser, R.; Waeny, M.; Sorci, M.; Llewellynn, T.; Fedorczak, C.; Larmoire, T.; Herbst, M.; Seirafi, A.; Seirafi, K. Eyes of Things. Sensors 2017, 17, 1173.

AMA Style

Deniz O, Vallez N, Espinosa-Aranda JL, Rico-Saavedra JM, Parra-Patino J, Bueno G, Moloney D, Dehghani A, Dunne A, Pagani A, Krauss S, Reiser R, Waeny M, Sorci M, Llewellynn T, Fedorczak C, Larmoire T, Herbst M, Seirafi A, Seirafi K. Eyes of Things. Sensors. 2017; 17(5):1173.

Chicago/Turabian Style

Deniz, Oscar, Noelia Vallez, Jose L. Espinosa-Aranda, Jose M. Rico-Saavedra, Javier Parra-Patino, Gloria Bueno, David Moloney, Alireza Dehghani, Aubrey Dunne, Alain Pagani, Stephan Krauss, Ruben Reiser, Martin Waeny, Matteo Sorci, Tim Llewellynn, Christian Fedorczak, Thierry Larmoire, Marco Herbst, Andre Seirafi, and Kasra Seirafi. 2017. "Eyes of Things" Sensors 17, no. 5: 1173.

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