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Parking Occupancy Detection Using Thermal Camera
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.ORCID-id: 0000-0002-2078-3327
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0002-1429-2345
2019 (Engelska)Ingår i: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, 2019, s. 483-490Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2019. s. 483-490
Nyckelord [en]
Convolutional Neural Network, Detectors, Thermal Camera
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-30153DOI: 10.5220/0007726804830490Scopus ID: 2-s2.0-85067576912ISBN: 978-989-758-374-2 (tryckt)OAI: oai:DiVA.org:du-30153DiVA, id: diva2:1321752
Konferens
5th International Conference on Vehicle Technology and Intelligent Transport Systems, May 3-5 2019, Heraklion, Greece
Tillgänglig från: 2019-06-10 Skapad: 2019-06-10 Senast uppdaterad: 2019-07-01Bibliografiskt granskad

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Paidi, VijayFleyeh, Hasan

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