Estimating the physical effort of human poses

Authors:
Yinpeng Chen;Hari Sundaram;Jodi James
Affiliations:
Arts, Media and Engineering, Arizona State University, Tempe, AZ;Arts, Media and Engineering, Arizona State University, Tempe, AZ;Arts, Media and Engineering, Arizona State University, Tempe, AZ
Venue:
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Year:
2006

Citing 4
Cited 0

Computer Vision: A Modern Approach

Computer Vision: A Modern Approach
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on support vector regression

Statistics and Computing
Acquiring and validating motion qualities from live limb gestures

Graphical Models

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the problem of estimating the effort required to maintain a static pose by human beings. The problem is important in developing effective pose classification as wells as in developing models of human attention. We estimate the human pose effort using two kinds of body constraints – skeletal constraints and gravitational constraints. The extracted features are combined together using SVM regression to estimate the pose effort. We tested our algorithm on 55 poses with different annotated efforts with excellent results. Our user studies additionally validate our approach.