Abstract
This paper proposes a new local texture descriptor, called quad binary pattern (QBP). Compared with local binary pattern (LBP), the QBP is with stronger robustness for feature extraction under complex scene (e.g., luminance change, similar target and background color) and with lower computational complexity. To demonstrate its effectiveness, the proposed QBP is further applied on the mean-shift tracking, in which a joint color-QBP model is developed to effectively represent the color and texture characteristics of the target region. Extensive simulation results have demonstrated that the proposed algorithm is able to improve the tracking speed and accuracy, compared with the standard mean-shift tracking and joint color-LBP model based mean-shift tracking.
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