Title
An accelerated human motion tracking system based on voxel reconstruction under complex environments
Abstract
In this paper, we propose an automated and markless human motion tracking system, including voxel acquisition and motion tracking. We first explore the problem of voxel reconstruction under a complex environment. Specifically, the procedure of the voxel acquisition is conducted under cluttered background, which makes the high quality silhouette unavailable. An accelerated Bayesian sensor fusion framework combining the information of pixel and super-pixel is adopted to calculate the probability of voxel occupancy, which is achieved by focusing the computation on the image region of interest. The evaluation of reconstruction result is given as well. After the acquisition of voxels, we adopt a hierarchical optimization strategy to solve the problem of human motion tracking in a high-dimensional space. Finally, the performance of our human motion tracking system is compared with the ground truth from a commercial marker motion capture. The experimental results show the proposed human motion tracking system works well under a complex environment.
Year
DOI
Venue
2009
10.1007/978-3-642-12304-7_30
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
markless human motion tracking,human motion tracking,accelerated human motion tracking,complex environment,voxel occupancy,voxel reconstruction,proposed human motion tracking,motion tracking,voxel acquisition,human motion tracking system,commercial marker motion,tracking system,motion capture,region of interest,sensor fusion,ground truth
Voxel,Computer vision,Motion capture,Motion field,Pattern recognition,Computer science,Silhouette,Tracking system,Sensor fusion,Artificial intelligence,Motion estimation,Match moving
Conference
Volume
Issue
ISSN
5995 LNCS
PART 2
16113349
ISBN
Citations 
PageRank 
3-642-12303-1
5
0.55
References 
Authors
20
4
Name
Order
Citations
PageRank
Junchi Yan189183.36
Yin Li279735.85
Enliang Zheng31707.58
Yuncai Liu41234185.16