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A conventional color histogram is sensitive to noisy interference such as lighting changes and quantization errors. Therefore the tracker based on conventional color histogram is unreliable or even failed under varying illumination. In this paper we propose a dynamically corrected fuzzy color histogram based kernel tracking algorithm, which utilizes the local background information around tracking target to dynamically correct its fuzzy color histogram model and eliminates the sensitive of conventional color histogram to illumination change and noise. The algorithm is tested on several image sequences and shown that it can smooth the similarity surface and achieve robust and reliable frame-rate tracking results under varying illumination conditions.