Domain and View-Point Agnostic Hand Action Recognition

Sabater, A. (Universidad de Zaragoza) ; Alonso, I. (Universidad de Zaragoza) ; Montesano, L. (Universidad de Zaragoza) ; Murillo, A.C. (Universidad de Zaragoza)

Resumen: Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.

Idioma: Inglés

DOI: 10.1109/LRA.2021.3101822

Año: 2021

Publicado en: IEEE Robotics and Automation Letters 6, 4 (2021), 7823-7830
ISSN: 2377-3766

Factor impacto JCR: 4.321 (2021)

Categ. JCR: ROBOTICS rank: 11 / 30 = 0.367 (2021) - Q2 - T2

Factor impacto CITESCORE: 8.0 - Engineering (Q1) - Mathematics (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 2.206 - Artificial Intelligence (Q1) - Biomedical Engineering (Q1) - Mechanical Engineering (Q1) - Control and Optimization (Q1) - Control and Systems Engineering (Q1) - Computer Vision and Pattern Recognition (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17R
Financiación: info:eu-repo/grantAgreement/ES/MICIU-AEI-FEDER/PGC2018-098817-A-I00

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)