Paper Info
Title
A mean shift algorithm based on modified Parzen window for small target tracking
Abstract
This paper addresses the problem of small scale target tracking. The divided-by-zero problem in the weight computation of mean shift algorithm and its associated tracking interrupt problem are presented. To tackle these problems, the Parzen window density estimation method is modified to interpolate the histogram of the target candidate. Then the Kullback-Leibler distance is employed as a new similarity measure between the target model and the target candidate. Its corresponding weight computation and new location expressions are derived. On the basis of these works, we propose a new small target tracking algorithm using mean shift framework. The tracking experiments for real world video sequences show that the proposed algorithm can track the target successively and accurately. It can successfully track very small targets with only 6×12 pixels.
Year
DOI
Venue
2010
10.1109/ICASSP.2010.5495375
ICASSP
Keywords
Field
DocType
histogram interpolation,video sequences,mean shift algorithm,target tracking,parzen window,kullback-leibler distance,small target tracking,independent component analysis,similarity measure,image sequences,mean shift,modified parzen window,parzen window density estimation method,divided-by-zero problem,video surveillance,histograms,kernel,computational modeling,information science,pixel,lighting,color,kullback leibler distance,interpolation,pulse width modulation,density estimation,clustering algorithms
Density estimation,Computer vision,Histogram,Pattern recognition,Similarity measure,Computer science,Pixel,Artificial intelligence,Mean-shift,Cluster analysis,Kernel density estimation,Computation
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4244-4296-6
978-1-4244-4296-6
2
PageRank 
References 
Authors
0.65
3
4
Name
Order
Citations
PageRank
CHEN JianJun1226.74
Guocheng An231.71
Suofei Zhang3347.26
Zhenyang Wu415417.52