RT Journal Article
A1 J. Ning
A1 L. Zhang
A1 D. Zhang
A1 C. Wu

PB iet
T1 Robust mean-shift tracking with corrected background-weighted histogram
JN IET Computer Vision
VO 6
IS 1
SP 62
OP 69
AB 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.
K1 mean-shift iteration
K1 target localisation
K1 background-weighted histogram algorithm
K1 mean-shift tracking
DO https://doi.org/10.1049/iet-cvi.2009.0075
UL https://digital-library.theiet.org/;jsessionid=132ksetp3hr8f.x-iet-live-01content/journals/10.1049/iet-cvi.2009.0075
LA English
SN 1751-9632
YR 2012
OL EN