A More Robust Mean Shift Tracker on Joint Color-CLTP Histogram

Автор: Pu Xiaorong, Zhou Zhihu

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 12 vol.4, 2012 года.

Бесплатный доступ

A more robust mean shift tracker using the joint of color and Completed Local Ternary Pattern (CLTP) histogram is proposed. CLTP is a generalization of Local Binary Pattern (LBP) which can be applied to obtain texture features that are more discriminant and less sensitive to noise. The joint of color and CLTP histogram based target representation can exploit the target structural information efficiently. To reduce the interference of background in target localization, a corrected background-weighted histogram and background update mechanism are adapted to decrease the weights of both prominent background color and texture features similar to the target object. Comparative experimental results on various challenging videos demonstrate that the proposed tracker performs favorably against several variants of state-of-the-art mean shift tracker when heavy occlusions and complex background changes exist.

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MeanShift, Object Tracking, Completed Local Ternary Pattern, Joint Color-CLTP Histogram

Короткий адрес: https://sciup.org/15012511

IDR: 15012511

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