Deformable parts model target tracking methods becomes an active research due to its effectiveness in tackling partial occlusion and deformation issues of targets. When partial occlusion or deformation occurs, deformable parts model trackers could achieve accurate tracking via the uncovered reliable parts. Most of the part-based trackers initialize the number and size of parts manually. In practical tracking systems, it is difficult to provide the interaction to select parts manually. Meanwhile, manual parts selection method might be affected by subjective factors. Aimed at the problems mentioned, automatic parts selection method based on multi-feature fusion was proposed. Firstly, the saliency measure based on human visual attention mechanism was applied to describe the salient region of target template. Secondly, edge direction dispersion was employed to describe the richness of texture details. After obtaining the joint suitable-matching confidence map, the number and size of parts were adaptively selected according to the pixel area and aspect ratio of the target. Finally, the parts were selected according to the joint suitable-matching confidence. Experimental results show that the proposed method can achieve more tracking precision compared with the current deformable parts model target tracking algorithm which selects the parts manually.