Based on the idea of machine learning and the sufficient appearance, a mixture random Naive Bayes visual tracker with online texture and shape feature selection is proposed. The texture and shape of global and local region is described with binary feature of intensity and pyramid histogram of oriented gradients using normalized spatial pyramid. An online mixture of Naive Bayes classifier is designed and realized according to binary and multimodel description. The classifier predicts the class posterior probability to generate the confidence map, then the tracker analyzes the confidence map to track the object, learns the appearance with maximum likelihood estimation, and selects the feature with cross validation. Compared with homogeneous methods, the tracker is evaluated with performance and complexity based on benchmarks. The experimental results show that the tracker has certain adaption to illumination change and partial occlusion, and fast execution speed as well as little memory space.