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Title:Spremljanje gostote in hitrosti prometa z uporabo kamere na platformi RPI : magistrsko delo
Authors:ID Kozole, Jure (Author)
ID Verber, Domen (Mentor) More about this mentor... New window
Files:.pdf MAG_Kozole_Jure_2025.pdf (2,33 MB)
MD5: D57344305AECD08BBCC57458891E5D47
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo obravnava razvoj sistema za spremljanje gostote in hitrosti prometa z uporabo kamere na platformi Raspberry Pi in pospeševalnika Coral TPU. Namen raziskave je zasnova cenovno dostopnega in energetsko učinkovitega sistema, ki lokalno v realnem času obdeluje videoposnetke brez zunanje infrastrukture. Sistem temelji na metodah računalniškega vida in umetne inteligence ter omogoča zaznavanje, sledenje, razvrščanje in ocenjevanje hitrosti vozil. Za zaznavanje je uporabljen prilagojen obstoječ model, za sledenje pa algoritem SORT, medtem ko se hitrost ocenjuje z analizo zaporednih slik prek več virtualnih črt brez dodatnih senzorjev. Delo prikazuje celoten proces načrtovanja, implementacije in delovanja sistema na robni napravi
Keywords:Računalniški vid, Umetna inteligenca, Zaznavanje vozil, Sledenje objektov, Ocena hitrosti, Robne naprave, Raspberry Pi, Coral TPU
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[J. Kozole]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (65 str.))
PID:20.500.12556/DKUM-95983 New window
UDC:[004.8:004.93]:656.021(043.2)
COBISS.SI-ID:266816515 New window
Publication date in DKUM:22.12.2025
Views:113
Downloads:29
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:18.11.2025

Secondary language

Language:English
Title:Monitoring traffic density and speed using a camera on the RPI platform
Abstract:The master's thesis focuses on the development of a system for monitoring traffic density and vehicle speed using a camera on the Raspberry Pi platform with a Coral TPU accelerator. The aim of the research is to design a cost-effective and energy-efficient system capable of processing video streams locally and in real time without external infrastructure. The system is based on computer vision and artificial intelligence methods, enabling vehicle detection, tracking, classification, and speed estimation. A customized detection model is used together with the SORT algorithm for object tracking, while vehicle speed is estimated by analyzing consecutive frames using multiple virtual lines without additional sensors. The thesis presents the complete process of system design, implementation, and operation on an edge device.
Keywords:Computer vision, Artificial intelligence, Vehicle detection, Object tracking, Speed estimation, Edge devices, Raspberry Pi, Coral TPU


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