Bayesian network based human pose estimation

Authors:
Do Joon Jung;Kyung Su Kwon;Hang Joon Kim
Affiliations:
Department of Computer Engineering, Kyungpook National University, Daegu, Republic of Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Republic of Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Republic of Korea
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 8
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning in graphical models

Learning in graphical models
Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems
Human Activity Recognition Using Multidimensional Indexing

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot competition using gesture based interface

IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Multiple frame motion inference using belief propagation

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Recognition of human action for game system

AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a method for human pose estimation from single camera images. The human body configuration is modeled by a Bayesian network. In this model, state nodes represent an aspect ratio, a length of long axis, a degree of tilt, a 2D center position and 2D position of body joints of each body parts and edges represent spatial constraint. We estimate the human pose through optimization of the model parameters based on simple person model. The model parameters are estimated from cues such as a color information, ratio of the length of long axes between node and parent node, and distance of body joints between node and parent node. For the estimation of dynamic human pose, we use temporal constraint. We apply the proposed method to the videos obtained from a gesture-based game control system and evaluate the performance using ground truth method. As a result, the proposed method can automatically label upper body pose from videos and it is appropriate method for feature extraction in the frontal view-based gesture recognition system.