Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking

Recently, fusion of low- and high-dimensional approaches shows its success in the generic human motion tracking. However, how to choose the trackers adaptively according to the motion types is still a challenging problem. This paper presents a trackers sampling approach for generic human motion trac...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the 21st International Conference on Pattern Recognition (ICPR2012) pp. 898 - 901
Main Authors Ye Liu, Jinshi Cui, Huijing Zhao, Hongbin Zha
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2012
Subjects
Annealing
Computational modeling
Humans
Joints
Legged locomotion
Sampling methods
Tracking
Online AccessGet full text

Cover

More Information
Summary:Recently, fusion of low- and high-dimensional approaches shows its success in the generic human motion tracking. However, how to choose the trackers adaptively according to the motion types is still a challenging problem. This paper presents a trackers sampling approach for generic human motion tracking using both low- and high-dimensional trackers. Gaussian Process Dynamical Model(GPDM) is trained to learn the motion model of low-dimensional tracker, and it performs better on specific motion types. Annealed Particle Filtering(APF) shows its advantage in the tracking without limitation on motion types. We combine both of the two methods and automatically sample trackers according to the motion types that it is tracking on. To improve performance, trackers communication is adopt to keep the better state of trackers. The approach facilitates tracking of generic motions with low particle numbers.
ISBN:9781467322164
1467322164
ISSN:1051-4651
2831-7475