Project Houseleek - A Case Study of Applied Object Recognition Models in Internet of Things

Published in Proceedings of the 42nd international convention MIPRO, 2019

Available at: http://docs.mipro-proceedings.com/cts/04_cts_5327.pdf

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.

@inproceedings{MIPRO19_1,
    title={Project Houseleek - A Case Study of Applied Object Recognition Models in Internet of Things},
    author={Knezovic, Jure and Pervan, Branimir and Relja, Zvonimir and Knezovic, Josip},
    booktitle={2019 proceedings of the 42nd international convention MIPRO},
    pages={1051--1055},
    year={2019},
    organization={IEEE}
}