Title:
CBWH_IET_Computer-Vision
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Description: The background-weighted histogram (BWH) algorithm proposed in [2] attempts to
reduce the interference of background in target localization in mean shift tracking. However,
in this paper we prove that the weights assigned to pixels in the target candidate region by
BWH are proportional to those without background information, i.e. BWH does not introduce
any new information because the mean shift iteration formula is invariant to the scale
transformation of weights. We then propose a corrected BWH (CBWH) formula by
transforming only the target model but not the target candidate model. The CBWH scheme
can effectively reduce background’s interference in target localization. The experimental
results show that CBWH can lead to faster convergence and more accurate localization than
the usual target representation in mean shift tracking. Even if the target is not well initialized,
the proposed algorithm can still robustly track the object, which is hard to achieve by the
conventiona
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File list (Check if you may need any files):
CBWH_IET_Computer Vision\CBWH_Demo.asv
........................\CBWH_Demo.m
........................\rgbPDF.asv
........................\rgbPDF.m
........................\rgbPDF_bg.m
........................\rgbTracking.m
........................\rgbTracking_BWH.m
........................\select.m
........................\test video\ball.avi
........................\..........\player.avi
........................\.racking result\trackingresult2.avi
........................\test video
........................\tracking result
CBWH_IET_Computer Vision
