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Counting Cars and Determining the Vacancy of a Parking Lotusing Neural Networks
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A lot of time, energy and money is being wasted when people are trying tond a parking lot. These elements could be reduced if the driver is provided vacancy information of a parking lot beforehand. In this thesis Google's Object Detection API is implemented and two pre-trained models are being used on the PKLot dataset to detect and count the number of cars in a parking lot. The models are based on a Region-based Convolutional Neural Network (R-CNN) which is explained in more detail. The models are compared with each other and its result presented. The result is presented with three factors in focus, the number of predictions made by the models, the number of cars a model missed to predict and how many objects that were wrongfully predicted. This was then tested on a Raspberry PI with the purpose to avoid using a remote computer for the image processing and prevent potential laws regarding camera surveillance. Finally, we determine if this functionality can actually be delivered using state-of-the-art technology.

Place, publisher, year, edition, pages
2018. , p. 53
Series
UMNAD ; 1142
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-149689OAI: oai:DiVA.org:umu-149689DiVA, id: diva2:1223834
External cooperation
Knowit
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf