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dc.contributor.authorTirado, Guilleum Budia
dc.contributor.authorSemwal, Sudhanshu
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T08:53:44Z
dc.date.available2021-08-31T08:53:44Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 115-124.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45016
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectdronycs
dc.subjectARcs
dc.subjectparkovištěcs
dc.subjectnavigacecs
dc.subjecthluboké učenícs
dc.titleAutonomous Parking Spot Detection System for Mobile Phones using Drones and Deep Learningen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedMany parking lot facilities suffer from capacity over-loads and many times there are no monitoring tools toprovide feedback. As a consequence, the people, want-ing to park, become frustrated as there is considerablyloss of time. In this paper, we present a novel proto-type of an automatic parking-lot analysis platform us-ing image-based machine learning to (a) guide a droneautonomously; and (b) to process useful information tobe handled into a smartphone application to communi-cate with the parking lot users.We have collected a reasonable amount of test imagesto build a classification model using Convolutional neu-ronal networks (CNNs) to classify parking lot images,and build different object detection models to identifyfree and occupied parking spots. Those models havebeen exported to the back-end module of our platformso it can control the drone and record the computed in-formation to its database. In addition, we have imple-mented an iOS application that requests and displaysthe parking lot status and its empty spots.We have been able to prove that this prototype is fea-sible, functional, and opens a path towards future im-provements and refinements. The flight control and thedata classification algorithms have been shown to workusing the machine learning models. In summary, wefound a clear and and concise way to display useful in-formation in real time to our users.en
dc.subject.translateddronesen
dc.subject.translatedARen
dc.subject.translatedparking loten
dc.subject.translatednavigationen
dc.subject.translateddeep learningen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.13
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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