Appearance model plays an important role in the human pose estimation. To improve the estimation accuracy, how to set up the appearance model by using the histogram of oriented gradient (HOG) and color features is studied. The sub-classifier for each cell unit of the body part is built by using the support vector data description ( SVDD ) algorithm, and then the HOG-based appearance model is constructed by the linear combination of sub-classifiers with different weights. The specific location probability is learned by using those states, which has higher similarity with the HOG-based appearance model, and the corresponding color histogram is calculated, which is the color-based appearance model. According to the illumination and the color contrast between the clothing and background in the static image to be proceeded, the weights of two appearance models are determined, and then two appearance models are combined linearly to construct appearance model based on the fusion of the HOG and color features. The proposed appearance model is used for human pose estimation, and the experimental results show it is more effectively and gets higher pose estimation accuracy.%部位外观模型在人体姿态估计中起着关键作用。为提高人体姿态估计的准确度，对如何利用梯度方向直方图( HOG)与颜色特征建立外观模型进行研究。利用支持向量数据描述算法( SVDD)对部位的所有细胞单元构造子分类器，将所有子分类器按照不同权值进行线性组合，建立基于HOG特征的外观模型；利用与基于HOG特征的外观模型之间似然度较高的部位状态学习定位概率，根据定位概率求得的颜色直方图即为基于颜色特征的外观模型；根据待处理静态图像的光照条件和人体着装及背景的颜色对比度可确定分别基于HOG和颜色特征的外观模型的权值；根据相应权值对两种外观模型进行线性组合，建立基于HOG和颜色特征融合的部位外观模型。将所提外观模型用于人体姿态估计，实验结果表明，该外观模型更加有效，获得更高的人体姿态估计准确度。
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