Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Velazquez, Eric Marin | |
dc.contributor.author | Semwal, Sudhanshu | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2021-08-31T09:00:26Z | |
dc.date.available | 2021-08-31T09:00:26Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 125-134. | en |
dc.identifier.isbn | 978-80-86943-34-3 | |
dc.identifier.issn | 2464-4617 | |
dc.identifier.issn | 2464–4625(CD/DVD) | |
dc.identifier.uri | http://hdl.handle.net/11025/45017 | |
dc.format | 10 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | drony | cs |
dc.subject | navigace | cs |
dc.subject | hluboké učení | cs |
dc.subject | sledování osoby | cs |
dc.title | Using Autonomous Drone Interactions towards MobilePersonal Spaces for Indoor Environments | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | We propose an extension of a recent work using convo-lutional neural networks and drones, such as Learning tofly by using DroNet [8] that can possibly safely drive adrone autonomously. The combination of (i) the DroNetarchitecture and weights to apply to CNNs to avoid thecrashes; (ii) combining it with DLIB tracker, a corre-lation implemented tracker based on Danelljan et al.’spaper [3] work; (iii) the extraction of descriptors usingSpeeded Up Robust Features [1]; and (iv) Fast Libraryfor Approximate Nearest Neighbors [10] for the featurematching – leads a drone to track any object and avoidcrashes autonomously without any prior informationabout the object. The main goal is to create a partnershipbetween the drone(s) and the participant as the dronefollows the participant and avoids collisions. Our workextends existing methods to also included a way for adrone to follow a person even if the person is hiddenfor a few frames. Our algorithms also work in low/poorambient light satisfactorily. In future, our technique canbe used to provide novel indoor applications for drones. | en |
dc.subject.translated | drones | en |
dc.subject.translated | navigation | en |
dc.subject.translated | deep learning | en |
dc.subject.translated | tracking a person | en |
dc.identifier.doi | https://doi.org/10.24132/CSRN.2021.3101.14 | |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG 2021: Full Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
I23.pdf | Plný text | 11,98 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/45017
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