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Title:Razpoznava objektov ter napovedovanje njihovega obnašanja v aplikacijah za avtonomno vožnjo : magistrsko delo
Authors:ID Kolar, Niko (Author)
ID Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Kolar_Niko_2024.pdf (3,98 MB)
MD5: FC668A8C095C525586B45391844728FE
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu smo se osredotočili na raziskovanje in implementacijo sistema za avtonomno vožnjo, ki služi kot osnova za sprejemanje odločitev. Informacije iz okolice vozila pridobivamo zgolj s pomočjo kamere. Za implementacijo smo uporabili dve vrsti kamer, in sicer monokularno ter stereo. Nad podatki monokularne kamere smo implementirali algoritme za prepoznavanje objektov v okolici vozila, sledenje tem objektom skozi zaporedje slik, zaznavanje voznega pasu, določanje pozicije vozila na njem in ocenjevanje časa do roba vozišča. Podatke stereo kamere smo uporabili za določanje razdalje do prepoznanih objektov ter konstrukcijo 3D strukture okolice premikajočega se vozila.
Keywords:avtonomna vožnja, stereo vid, računalniški vid, konvolucijske nevronske mreže
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[N. Kolar]
Year of publishing:2024
Number of pages:1 spletni vir (1 datoteka PDF (VIII, 89 f.))
PID:20.500.12556/DKUM-90165 New window
UDC:004.352.242:629.4.072(043.2)
COBISS.SI-ID:219372803 New window
Publication date in DKUM:14.10.2024
Views:0
Downloads:16
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:23.08.2024

Secondary language

Language:English
Title:Object recognition and behavior prediction in autonomous driving applications
Abstract:In the master's thesis, we focused on researching and implementing a system for autonomous driving, serving as a foundation for decision-making. Information from the vehicle's surroundings is obtained solely through the use of a camera. For the implementation, we utilized two types of cameras, namely monocular and stereo. Using the data from monocular camera, we implemented algorithms for object recognition in the vehicle's surroundings, tracking these objects through a sequence of images, detecting the driving lane, determining the vehicle's position on it, and estimating the time to the lane's edge. Data from the stereo camera were employed for determining the distance to recognized objects and constructing a 3D structure of the moving vehicle's surroundings.
Keywords:autonomous driving, stereo vision, computer vision, convolutional neural networks


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