In this paper, the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking framework. Firstly, we extract local patches within a candidate and then utilize the LLC algorithm to encode these patches. Based on these codes, we exploit pyramid max pooling strategy to generate a richer feature histogram. The feature histogram which integrates holistic and part-based features can be more discriminative and representative. Besides, an occlusion handling strategy is utilized to make our tracker more robust. Finally, an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and candidates. For tracking, target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes, and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking algorithm. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines.