Title
Estimating human 3D pose from Time-of-Flight images based on geodesic distances and optical flow
Abstract
In this paper, we present a method for human full-body pose estimation from Time-of-Flight (ToF) camera images. Our approach consists of robustly detecting anatomical landmarks in the 3D data and fitting a skeleton body model using constrained inverse kinematics. Instead of relying on appearance-based features for interest point detection that can vary strongly with illumination and pose changes, we build upon a graph-based representation of the ToF depth data that allows us to measure geodesic distances between body parts. As these distances do not change with body movement, we are able to localize anatomical landmarks independent of pose. For differentiation of body parts that occlude each other, we employ motion information, obtained from the optical flow between subsequent ToF intensity images. We provide a qualitative and quantitative evaluation of our pose tracking method on ToF sequences containing movements of varying complexity.
Year
DOI
Venue
2011
10.1109/FG.2011.5771333
FG
Keywords
Field
DocType
differential geometry,pose estimation,tracking,optical flow,adaptive optics,geodesic distance,three dimensional,time of flight,inverse kinematics,optical imaging
Computer vision,Graph,Inverse kinematics,Interest point detection,Pose,Artificial intelligence,Time of flight,Geography,Optical flow,Geodesic,Adaptive optics
Conference
Citations 
PageRank 
References 
39
1.49
14
Authors
4
Name
Order
Citations
PageRank
Loren Arthur Schwarz11918.57
Artashes Mkhitaryan21144.07
Diana Mateus341732.74
Nassir Navab46594578.60