Authors:
Enrique Martinez
1
;
Oliver Nina
2
;
Antonio J. Sánchez
1
and
Carlos Ricolfe
3
Affiliations:
1
Universitat Politecnica de Valencia, Spain
;
2
University of Central Florida, United States
;
3
Universitat Politecnica de València, Spain
Keyword(s):
Human Pose Estimation, DPM, RGBD Images, Inverse Kinematics.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
Abstract:
The Deformable Parts Model (DPM) is a standard method to perform human pose estimation on RGB images,
3 channels. Although there has been much work to improve such method, little work has been done on
utilizing DPM on other types of imagery such as RGBD data. In this paper, we describe a formulation of
the DPM model that makes use of depth information channels in order to improve joint detection and pose
estimation using 4 channels. In order to offset the time complexity and overhead added to the model due to
extra channels to process, we propose an optimization for the proposed algorithm based on solving direct and
inverse kinematic equations, that form we can reduce the interested points reducing, at the same time, the time
complexity. Our results show a significant improvement on pose estimation over the standard DPM model on
our own RGBD dataset and on the public CAD60 dataset.