Pregled bibliografske jedinice broj: 1021658
Project Houseleek - A Case Study of Applied Object Recognition Models in Internet of Things
Project Houseleek - A Case Study of Applied Object Recognition Models in Internet of Things // 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics / Skala, Karolj (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2019. str. 1051-1055 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1021658 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Project Houseleek - A Case Study of Applied Object Recognition Models in Internet of Things
Autori
Knezović, Jure ; Pervan, Branimir ; Relja, Zvonimir ; Knezović, Josip
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics
/ Skala, Karolj - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2019, 1051-1055
ISBN
978-1-5386-9296-7
Skup
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2019)
Mjesto i datum
Opatija, Hrvatska, 20.05.2019. - 24.05.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
deep learning ; smart home ; internet of things ; face recognition
Sažetak
Nowadays, the gap between academic work and practical application of that work is rapidly diminishing. This fact can be backed by several factors: the increase in availability of the research results, as well as research artifacts ; the rise in the level of education in general ; the availability of broadband networks and the more affordable prices of the technology used for research. Also, due to the pervasion of the technology in all spheres of society, there is an emergence of new possibilities of applying disruptive technologies at all levels, including homes or workplaces of individual users. This paper presents Project Houseleek: a multilayer system that utilizes disruptive technologies to enhance and facilitate access to individual premises in smart areas. On the authentication layer, the system uses disruptive deep learning technologies to identify or learn itself a person in a real-world environment from an image grabbed in relatively rough conditions, while at the authorization layer it learns at runtime the access rights to specific parts of the smart area for that person. The testing system is implemented at the Department of Control and Computer Engineering, Faculty of Electrical Engineering and Computing where it exceeded the expectations of the users on both authentication and authorization layers.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb