Paper Info
Title
Efficient Mean Shift Particle Filter for Sperm Cells Tracking
Abstract
Tracking of human sperm cells is a challenging task in computer vision due to the motion uncertainty. In this paper, we propose an efficient and effective algorithm for sperm cells tracking which attempts to capture the motion uncertainty of the target object. The tracking problem is formulated within the Bayesian filter framework. To address this problem, we incorporate an orientation adaptive mean shift optimization into particle filter framework. The proposed tracking algorithm significantly improves the sampling efficiency during the tracking process. We provide quantitative evaluations of the proposed method against existing tracking algorithms, and the experimental results demonstrate that our approach efficiently samples the object state, with accurate and robust tracking output for human sperm cells.
Year
DOI
Venue
2009
10.1109/CIS.2009.264
CIS (1)
Keywords
Field
DocType
bayesian filter,particle filtering (numerical methods),cellular biophysics,bayesian filter framework,mean shift particle filter,tracking problem,tracking algorithm,biomedical optical imaging,bayes methods,motion uncertainty,target tracking,effective algorithm,correlogram,proposed tracking algorithm,sperm cells tracking,sperm cells,efficient mean shift particle,human sperm cell,tracking process,mean shift,object detection,computer vision,adaptive mean shift optimization,particle filter,sperm cell tracking,sampling efficiency,robust tracking output,target object,medical image processing,image motion analysis,algorithm design and analysis,pixel,particle filters,optimization
Object detection,Computer vision,Algorithm design,Computer science,Particle filter,Tracking system,Sampling (statistics),Pixel,Artificial intelligence,Mean-shift,Correlogram,Machine learning
Conference
Volume
ISBN
Citations 
1
978-1-4244-5411-2
6
PageRank 
References 
Authors
0.62
2
2
Name
Order
Citations
PageRank
Xiuzhuang Zhou138020.26
Yao Lu27713.40