Target tracking is one of the most topic-active research and also the most important part in the field of computer vision. The typical deformable model target tracking algorithm decomposes each target into multi-sub-blocks, and computes the similarity of both the local areas of each target and the spatial location among each sub-block. However, these algorithms define the area and the number of sub-blocks manually. In the practical application, the tracking system can provide the interaction to select the tracking target real-timely. But it’s difficult to provide the interaction to select the sub-blocks. It means the selection of sub-blocks manually has limitation in the practical application. Aimed at the problems mentioned, this paper presents a method for automatic sub-blocks segmentation. The proposed method integrates the local contrast and the richness of texture details to get a measure function of sub-blocks. Saliency detection based on visual attention model was used to extract salient local contrast. The edge direction dispersion has been used to describe the richness of texture details. Then, the discrimination of each pixel in the target will be computed by the mentioned methods above. Finally, sub-blocks with high discrimination will be chosen for tracking. Experimental results show that the method proposed can achieve more tracking precision compared with the current deformable target tracking algorithm which selected the sub-blocks manually.