Deep Convolutional Neural Networks applied to Hand Keypoints Estimation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Santos, BM (Autor) FEUP; Pais, P (Autor) Outra; Ribeiro, FM (Autor) Outra; Lima, J (Autor) Outra; Gil Manuel Gonçalves (Autor) FEUP; Pinto, VH (Autor) FEUP

Ata de Conferência Internacional

Título: 2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Páginas: 93-98

IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Tomar, PORTUGAL, APR 26-27, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00Y-DQV

DOI: 10.1109/icarsc58346.2023.10129621

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6