摘要：针对采用静态目标表达模型容易导致跟踪失败的缺点，本文提出一种基于自适应特征生成模型的目标跟踪方法。在该方法中，将目标看作为有效跟踪信号，而背景则为随机噪声，在对目标的似然图像进行构建后，采用局部信噪比对当前目标所处特征空间的可跟踪性进行量化比较，选用信噪比最大的表达模型作为当前的特征跟踪模型。基于Mean Shift的目标跟踪实验表明，与采用静态目标表达模型相比，文中的算法具有更好的鲁棒性和可行性。

Abstract：According to the poor tracking ability adopting static feature model, an adaptive feature generating model based tracking program is present. In this program, the object is valid tracking signal, on the contrary, the background is noise constructing the likelihood maps a local SNR(Signal Noise Ratio) is computed to evaluate the tracking ability of current feature space, and the feature space with maximal SNR is selected as the optimal tracking feature space. Object tracking results based on mean shift demonstrated that the proposed method is more robust and feasible than the classical one.