Paper Info
Title
3D body pose estimation using an adaptive person model for articulated ICP
Abstract
The perception of persons is an important capability of today's robots that work closely together with humans. An operator may use, for example, gestures to refer to an object in the environment. In order to perceive such gestures, the robot has to estimate the body pose of the operator. We focus on the marker-less motion capture of a human body by means of an Iterative Closest Point (ICP) algorithm for articulated structures. An articulated upper body model is aligned with the depth measurements of an RGB-D camera. Due to the variability of the human body, we propose an adaptive body model that is aligned within the sensor data and iteratively adjusted to the person's body dimensions. Additionally, we preserve consistency with respect to self-collisions. Besides that, we use an inverse data assignment, that is particularly utile for articulated models. Experiments with measurements of a Microsoft Kinect camera show the advantage of the approach compared to the standard articulated ICP algorithm in terms of the root mean squared (RMS) error and the number of iterations the algorithm needs to converge. In addition, we show that our consistency checks enable to recover from situations where the standard algorithm fails.
Year
DOI
Venue
2011
10.1007/978-3-642-25489-5_16
ICIRA
Keywords
Field
DocType
body dimension,adaptive body model,rgb-d camera,articulated upper body model,standard algorithm,adaptive person model,microsoft kinect camera,standard articulated icp algorithm,human body,articulated structure,articulated model
Motion capture,Computer vision,Standard algorithms,Gesture,Computer science,Pose,Artificial intelligence,Articulated body pose estimation,Robot,Human–robot interaction,Iterative closest point
Conference
Volume
ISSN
Citations 
7102
0302-9743
14
PageRank 
References 
Authors
0.70
21
2
Name
Order
Citations
PageRank
David Droeschel129221.76
Sven Behnke21672181.84