Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21594
Title: Robust mean-shift tracking with corrected background-weighted histogram
Authors: Ning, J
Zhang, L 
Zhang, D 
Wu, C
Issue Date: 2012
Source: IET Computer vision, 2012, v. 6, no. 1, p. 62-69 How to cite?
Journal: IET Computer Vision 
Abstract: The background-weighted histogram (BWH) algorithm proposed by Comaniciu et al. attempts to reduce the interference of background in target localisation in mean-shift tracking. However, the authors prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, that is, BWH does not introduce any new information because the mean-shift iteration formula is invariant to the scale transformation of weights. Then a corrected BWH (CBWH) formula is proposed by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localisation. The experimental results show that CBWH can lead to faster convergence and more accurate localisation than the usual target representation in mean-shift tracking. Even if the target is not well initialised, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.
URI: http://hdl.handle.net/10397/21594
ISSN: 1751-9632
DOI: 10.1049/iet-cvi.2009.0075
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

120
Last Week
1
Last month
5
Citations as of Aug 21, 2017

WEB OF SCIENCETM
Citations

63
Last Week
0
Last month
0
Citations as of Aug 20, 2017

Page view(s)

58
Last Week
3
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.