106635 20211216131135.0 doi 10.1109/CVPRW50498.2020.00528 sideral 119935 ART-2020-119935 eng Barbed, O.L. Universidad de Zaragoza (orcid)0000-0001-8191-6261 Fine grained pointing recognition for natural drone guidance 2020 Access copy available to the general public Unrestricted Human action recognition systems are typically focused on identifying different actions, rather than fine grained variations of the same action. This work explores strategies to identify different pointing directions in order to build a natural interaction system to guide autonomous systems such as drones. Commanding a drone with hand-held panels or tablets is common practice but intuitive user-drone interfaces might have significant benefits. The system proposed in this work just requires the user to provide occasional high-level navigation commands by pointing the drone towards the desired motion direction. Due to the lack of data on these settings, we present a new benchmarking video dataset to validate our framework and facilitate future research on the area. Our results show good accuracy for pointing direction recognition, while running at interactive rates and exhibiting robustness to variability in user appearance, viewpoint, camera distance and scenery. info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17R info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-096367-B-I00 info:eu-repo/grantAgreement/ES/MICIU-FEDER/PGC2018-098817-A-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 1.122 2020 Electrical and Electronic Engineering 2020 Computer Vision and Pattern Recognition 2020 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Azagra, P. Universidad de Zaragoza (orcid)0000-0002-3567-3294 Teixeira, L. Chli, M. Civera, J. Universidad de Zaragoza (orcid)0000-0003-1368-1151 Murillo, A.C. Universidad de Zaragoza (orcid)0000-0002-7580-9037 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 2020-June (2020), 4480-4488 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. workshops IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2160-7508 2368922 http://zaguan.unizar.es/record/106635/files/texto_completo.pdf Postprint 2923128 http://zaguan.unizar.es/record/106635/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:106635 articulos driver 2021-12-16-13:04:48 ARTICLE