A review of 3D human pose estimation algorithms for markerless motion capture - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Computer Vision and Image Understanding Année : 2021

A review of 3D human pose estimation algorithms for markerless motion capture

(1) , (1) , (1) , (1)
1
Yann Desmarais
Denis Mottet
Pierre Slangen

Résumé

Human pose estimation is a very active research field, stimulated by its important applications in robotics, entertainment or health and sports sciences, among others. Advances in convolutional networks triggered noticeable improvements in 2D pose estimation, leading modern 3D markerless motion capture techniques to an average error per joint of 20 mm. However, with the proliferation of methods, it is becoming increasingly difficult to make an informed choice. Here, we review the leading human pose estimation methods of the past five years, focusing on metrics, benchmarks and method structures. We propose a taxonomy based on accuracy, speed and robustness that we use to classify de methods and derive directions for future research.
Fichier principal
Vignette du fichier
ASurveyofMarkerlessHumanMotionCaptureMethodAuthor.pdf (1003.82 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03344404 , version 1 (12-10-2021)

Identifiants

Citer

Yann Desmarais, Denis Mottet, Pierre Slangen, Philippe Montesinos. A review of 3D human pose estimation algorithms for markerless motion capture: review. Computer Vision and Image Understanding, 2021, 212, pp.103275. ⟨10.1016/j.cviu.2021.103275⟩. ⟨hal-03344404⟩
178 Consultations
110 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More