An Extensive Evaluation of Mean-Shift Tracking for Surveillance.
of mean shift is gradient ascent with the Bhattacharyya coefficient, multiple-part models, background exclusion, HSV or YUV colour space, 4 bins per colour
channel, a convergence value of 0.25–1.00 and a cube root profile kernel. As a baseline, we include tracking using normalised cross correlation (NCC) and find that NCC appears to outperform all variants of the mean-shift tracker. In order to compare mean-shift and NCC tracking across colour spaces we also define a version of NCC for multi-channel data.
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