Authors:
Fabrice Dieudonné Atrevi
;
Damien Vivet
;
Florent Duculty
and
Bruno Emile
Affiliation:
Univ. Orléans, France
Keyword(s):
Pose Estimation, 3D Pose, 3D Modeling, Skeleton Extraction, Shape Descriptor, Geometric Moment, Krawtchouk Moment.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Shape Representation and Matching
;
Video Surveillance and Event Detection
Abstract:
This work focuses on the problem of automatically extracting human 3D poses from a single 2D image. By
pose we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D
posture of the detected human. This problem is highly non-linear in nature and confounds standard regression
techniques. Our approach combines prior learned correspondences between silhouettes and skeletons
extracted from 3D human models. In order to match detected silhouettes with simulated silhouettes, we used
Krawtchouk geometric moment as shape descriptor. We provide quantitative results for image retrieval across
different action and subjects, captured from differing viewpoints. We show that our approach gives promising
result for 3D pose extraction from a single silhouette.