Authors:
Ilya Afanasyev
;
Massimo Lunardelli
;
Nicolò Biasi
;
Luca Baglivo
;
Mattia Tavernini
;
Francesco Setti
and
Mariolino De Cecco
Affiliation:
University of Trento, Italy
Keyword(s):
Superquadrics, RANSAC Fitting, Human Body Pose Estimation, 3D Object Localization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Pattern Recognition
;
Shape Representation and Matching
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
This paper presents a method for 3D Human Body pose estimation. 3D real data of the searched object is acquired by a multi-camera system and segmented by a special preprocessing algorithm based on clothing analysis. The human body model is built by nine SuperQuadrics (SQ) with a-priori known anthropometric scaling and shape parameters. The pose is estimated hierarchically by RANSAC-object search with a least square fitting 3D point cloud to SQ models: at first the body, and then the limbs. The solution is verified by evaluating the matching score, i.e. the number of inliers corresponding to a-piori chosen distance threshold, and comparing this score with admissible inlier threshold for the body and limbs. This method can be used for 3D object recognition, localization and pose estimation of Human Body.