20.500.12556/DKUM-80610 Pametna avtomatizacija vstopa v dom na osnovi interneta stvari z uporabo Raspberry Pi diplomsko delo Iot based smart home entrance automation using Raspberry Pi V zaključni nalogi s pomočjo platforme Raspberry Pi razvijemo in implementiramo lasten avtomatiziran sistem za vstop v dom. Osnovne dogodke zaznamo s pomočjo kamere in algoritma zaznave gibanja. Primerjamo in testiramo dve metodi zaznave gibanja na osnovi modela Gaussovih mešanic in Bayesove verjetnosti. Vstop v dom poteka na osnovi dvostopenjske avtentikacije, kjer se preverjata biometrična in brezkontaktna identifikacija. Biometrično identifikacijo izpolnimo že pri 80 % gotovosti prepoznave obraza, brezkontaktno pa s pomočjo zaznane naprave, ki pripada določenemu profilu. Z uporabo tehnologije interneta stvari avtomatiziran sistem samodejno upravlja z lučmi v prostoru glede na sončni vzhod in zahod, predvaja glasbo in regulira temperaturo zraka glede na letni čas. Celotni sistem testiramo 30 dni, kjer je bila odzivnost sistema manjša kot 5 sekund in uspešnost odločanja sistema večja kot 90 %. Uporabo sistema dodatno podpremo z enostavno namizno aplikacijo, ki uporabniku omogoča ogled video prenosa v živo, konfiguracijo osebnih profilov, ročno upravljanje vhodnih vrat in pregled zgodovine dogodkov. The thesis describes the development and implementation of an automated home entrance system, based on the Raspberry Pi platform. System primary events are detected by a camera and a motion detection algorithm. We compare and test two motion detection methods based on the Gaussian mixture model and the Bayesian probability. Home entrance is based on two-factor authentication where biometric and contactless identification is verified. Authentication is fulfilled with biometric identification in the case of 80 % certainty of face recognition and with the help of a contactless detected device that belongs to a specific profile. With the support of Internet of Things technology, the automated system automatically controls the lights in the room concerning sunrise and sunset, plays music, and regulates the air temperature according to the time of year. We tested the entire system for 30 days, where the system's responsiveness was lower than 5 seconds, and the decision-making efficiency of the system was greater than 90 %. The use of the system is supported with the desktop application that allows the user to watch live video streaming, configure personal profiles, manually operate the front door, and view event history. internet stvari avtomatizacija zaznava gibanja prepoznava obraza Raspberry Pi internet of things automation motion detection face recognition Raspberry Pi true true false [A. Jelen] Slovenski jezik Angleški jezik Diplomsko delo/naloga 2021-09-22 13:49:22 2021-10-18 13:15:48 2022-08-09 23:41:54 0000-00-00 00:00:00 2021 0 Maribor 0 Maribor IX, 38 f. 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93\'1(043.2) 88081667 UN_Jelen_Ana_2021.pdf UN_Jelen_Ana_2021.pdf 1 2973AEB9EAC8EA0F44950677D578E774 1c0b73da06d01a6f98eb4eb4e7d61f14ac5f126f6ab2d4fdc9b2ca71aa529234 142459a2-2f9e-11ec-acc3-00155d000105 20.500.12556/dkum/87f8189b-f1d1-4756-bfd8-8c4c81f96f19 https://dk.um.si/Dokument.php?lang=slv&id=155031 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0