Deep Convolutional Neural Networks applied to Hand Keypoints Estimation

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

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

Conference proceedings International

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

Pages: 93-98

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

Tomar, PORTUGAL, APR 26-27, 2023

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Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6