A Dynamic Features Selection Based Algorithm for 3D Objects Motion Estimation

In this paper, an approach of kinetic parameter estimation and real-time pose tracking for 3D moving objects is investigated. The main work includes two folds: firstly, an extended Kalman filter (EKF) is designed to estimate the kinetic parameter with a hybrid eye to hand/eye in hand multi-camera vi...

Full description

Saved in:
Bibliographic Details
Published in2009 third International Symposium on Intelligent Information Technology Application : 21-22 November 2009 Vol. 2; pp. 57 - 60
Main Authors Luo Xiang, Ping Guoxiang, Wei Bing, Zhong Linfeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Cameras
Computer vision
extended Kalman filter
feature selection
Information technology
Kinetic theory
Machine vision
Mechanical engineering
Motion estimation
Parameter estimation
pose estimation
Tracking
Visual servoing
Online AccessGet full text
ISBN9780769538594
0769538592
DOI10.1109/IITA.2009.312

Cover

More Information
Summary:In this paper, an approach of kinetic parameter estimation and real-time pose tracking for 3D moving objects is investigated. The main work includes two folds: firstly, an extended Kalman filter (EKF) is designed to estimate the kinetic parameter with a hybrid eye to hand/eye in hand multi-camera vision system. Secondly, a scheme of dynamic feature selection is proposed. One of the main innovations in this paper is that the maximum inscribed circle of the feature set involved in estimation is proposed to be the criterion of feature selection. Simulation results demonstrate that the accuracy of estimation can be obviously improved by using this strategy.
ISBN:9780769538594
0769538592
DOI:10.1109/IITA.2009.312