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Object tracking based on Mean Shift (MS) algorithm has been very successful and thus receives significant research interests. Unfortunately, traditional MS based tracking only utilizes the gradient of the similarity function (SF), neglecting completely higher-order information of SF. The paper regards MS based tracking as an optimization problem, and proposes to make use of both the Gradient and Hessian of SF. Specifically, we introduce Newton algorithm with constant, unit step and Newton withdoi:10.1109/icpr.2008.4761486 dblp:conf/icpr/XiaoL08 fatcat:v5jp7mhdm5awlhnq5mti6txoji