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An Extensive Evaluation of Mean-Shift Tracking for Surveillance.

Darren Caulfield, Kenneth Dawson-Howe

Abstract


We evaluate the mean-shift tracker in the video surveillance domain in order to identify its best configuration. We compare the basic gradient ascent tracker with brute-force approaches. We assess the performance of multiple-part models and background exclusion (separately and combined). We assess its use with 21 colour spaces, 5 histogram quantisations, 6 convergence parameter values and 5 kernel types. We find that the best variant
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.

Keywords


Mean-shift tracking; normalised cross-correlation; multiple-part models; background exclusion; gradient ascent; Bhattacharyya coefficient techniques.

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