Sistem za sledenje stikov z uporabo analize videa

magistrsko delo

Erik Kastelec (Author), Žiga Emeršič (Mentor), Danijel Skočaj (Co-mentor)

Abstract

Pandemija COVID-19 je pokazala pomembnost sledenja stikov za omejitev širjenja okužb. Težava sistemov za sledenje, ki zahtevajo uporabo aplikacij in s tem obremenjujejo uporabnike, je, da se ne uporabljajo, ko pandemije ni. V tem magistrskem delu je predstavljen sistem za iskanje bližnjih stikov, ki predstavlja neinvazivno rešitev sledenja stikov v stavbah. Sistem je sestavljen iz dveh podsistemov. Podsistem za nadzor kamer nam omogoča, da upoštevanje varne razdalje med osebami spremljamo v živo in ga enostavno integriramo v obstoječe sisteme kamer. Podsistem za iskanje bližnjih stikov pa nam omogoča, da najdemo vse bližnje stike določene osebe, a le, ko je to zahtevano. S tem prihranimo vire in vzdržujemo zasebnost obiskovalcev. Za potrebe zaznave in identifikacije oseb je bil razvit učinkovit model Eff-SeqNet, ki se lahko izvaja in uči na grafičnih procesnih enotah, ki so dostopne končnim uporabnikom. S tem smo pokazali, da lahko s smiselno izbiro arhitekture modela dosežemo dober kompromis med natančnostjo in hitrostjo zaznave. Poleg učinkovitega modela smo predstavili cevovod iskanja stikov, ki izrablja informacije o lokaciji in povezavah med kamerami in s tem izboljša robustnost in učinkovitost sistema.

Keywords

detekcija oseb;ponovna identifikacija;iskanje oseb;sledenje stikov;analiza videa;računalništvo in informatika;magisteriji;

Data

Language:	Slovenian
Year of publishing:	2023
Typology:	2.09 - Master's Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[E. Kastelec]
UDC:	004(043.2)
COBISS:	178646019
Views:	62
Downloads:	13
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Contact tracing system utilizing video analysis
Secondary abstract:	The COVID-19 pandemic highlighted the importance of contact tracing to limit the spread of infections. The problem with tracing systems that require the use of apps and other approaches, which burden users, is that they often go unused outside of a pandemic. This master's thesis introduces a system for identifying close contacts, offering a non-invasive solution for contact tracing within buildings. The system comprises two parts: the camera surveillance system allows for real-time monitoring of social distancing between individuals and can be easily integrated into existing camera systems. The close-contact detection system enables us to identify all the close contacts of a specific person, but only when necessary. This conserves resources and maintains the privacy of visitors. For the purposes of detection and identification of individuals, an efficient model named Eff-SeqNet was developed, which can be used on graphical processing units available to end users. With this, we demonstrated that by making a thoughtful choice in model architecture, a good balance between detection accuracy and speed can be achieved. In addition to the new efficient model, we introduced a person search pipeline utilizing information about position and connections between cameras, which improves the robustness as well as the efficiency of our system.
Secondary keywords:	person detection;person re-identification;person search;contact tracing;video analysis;computer science;computer and information science;master's degree;Sledilni sistemi (tehnika);Identifikacija;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS):	Master's thesis/paper
Study programme:	1000471
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	92 str.
ID:	21682020