Title:	Using Autonomous Drone Interactions towards MobilePersonal Spaces for Indoor Environments
Authors:	Velazquez, Eric Marin Semwal, Sudhanshu
Citation:	WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 125-134.
Issue Date:	2021
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/45017
ISBN:	978-80-86943-34-3
ISSN:	2464-4617 2464–4625(CD/DVD)
Keywords:	drony;navigace;hluboké učení;sledování osoby
Keywords in different language:	drones;navigation;deep learning;tracking a person
Abstract in different language:	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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2021: Full Papers Proceedings

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