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Parking Occupancy Detection Using Thermal Camera
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-2078-3327
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
2019 (English)In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, 2019, p. 483-490Conference paper, Published paper (Refereed)
Abstract [en]

Parking a vehicle is a daunting task during peak hours. The search for a parking space leads to congestion and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. This paper aims to identify parking occupancy in an open parking lot which consists of free parking spaces using a thermal camera. A thermal camera is capable of detecting vehicles in any weather and light conditions based on emitted heat and it can also be installed in public places with less restrictions. However, a thermal camera is expensive compared to a colour camera. A thermal camera can detect vehicles based on the emitted heat without any illumination. Vehicles appear bright or dark based on heat emitted by the vehicles. In order to identify vehicles, pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented in this paper. The detection rates of the detectors reduced with diminishing of heat in the vehicles. Modified Faster RCNN deep learning network produced better detection results compared to other detectors. However, the detection rates can further be improved with larger and diverse training dataset.

Place, publisher, year, edition, pages
2019. p. 483-490
Keywords [en]
Convolutional Neural Network, Detectors, Thermal Camera
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-30153DOI: 10.5220/0007726804830490Scopus ID: 2-s2.0-85067576912ISBN: 978-989-758-374-2 (print)OAI: oai:DiVA.org:du-30153DiVA, id: diva2:1321752
Conference
5th International Conference on Vehicle Technology and Intelligent Transport Systems, May 3-5 2019, Heraklion, Greece
Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2019-07-01Bibliographically approved

Open Access in DiVA

Parking occupancy detection(697 kB)96 downloads
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Paidi, VijayFleyeh, Hasan

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CiteExportLink to record
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Citation style
  • apa
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  • vancouver
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Language
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