Publication
Deep convolutional neural networks applied to hand keypoints estimation
| dc.contributor.author | Santos, Bruno M. | |
| dc.contributor.author | Pais, Pedro | |
| dc.contributor.author | Ribeiro, Francisco M. | |
| dc.contributor.author | Lima, José | |
| dc.contributor.author | Goncalves, Gil | |
| dc.contributor.author | Pinto, Vítor H. | |
| dc.date.accessioned | 2020-04-30T09:42:12Z | |
| dc.date.available | 2020-04-30T09:42:12Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Accurate estimation of hand shape and position is an important task in various applications, such as human-computer interaction, human-robot interaction, and virtual and augmented reality. In this paper, it is proposed a method to estimate the hand keypoints from single and colored images utilizing the pre-trained deep convolutional neural networks VGG-16 and VGG-19. The method is evaluated on the FreiHAND dataset, and the performance of the two neural networks is compared. The best results were achieved by the VGG-19, with average estimation errors of 7.40 pixels and 11.36 millimeters for the best cases of two-dimensional and three-dimensional hand keypoints estimation, respectively. | pt_PT |
| dc.description.sponsorship | The authors acknowledge the support of R&D Unit SYSTEC Base (UIDB/00147/2020) and Programmatic (UIDP/00147/2020) and the ARISE Associated Laboratory (LA/P/0112/2020), as well as the support of projects: Continental FoF, with reference POCI-01-0247-FEDER- 047512, co-funded by FEDER, through COMPETE 2020, Digitalizac¸ ˜ao da Arte Humana (Cibertoque), with reference POCI-01-0247-FEDER-072627, co-funded by FEDER, through COMPETE 2020 and Next-Gen Quality Control IoRT System with reference POCI-01-0247-FEDER-072616, co-funded by FEDER, through COMPETE 2020. | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Santos, Bruno M.; Pais, Pedro; Ribeiro, Francisco M.; Lima, José; Goncalves, Gil; Pinto, Vítor H. (2023). Deep convolutional neural networks applied to hand keypoints estimation. In IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). Tomar, Portugal – April 26-27, 2023. eISSN 2573-9387. p. 93-98 | pt_PT |
| dc.identifier.doi | 10.1109/ICARSC58346.2023.10129621 | |
| dc.identifier.eissn | 2573-9387 | |
| dc.identifier.issn | 2573-9360 | |
| dc.identifier.uri | http://hdl.handle.net/10198/21876 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Hand keypoints estimation | pt_PT |
| dc.subject | Convolutional neural network | pt_PT |
| dc.subject | VGG | pt_PT |
| dc.subject | FreiHAND | |
| dc.title | Deep convolutional neural networks applied to hand keypoints estimation | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.title | IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) | pt_PT |
| person.familyName | Lima | |
| person.givenName | José | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.scopus-author-id | 55851941311 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | d88c2b2a-efc2-48ef-b1fd-1145475e0055 | |
| relation.isAuthorOfPublication.latestForDiscovery | d88c2b2a-efc2-48ef-b1fd-1145475e0055 |