Most visual trackers focus on short-term tracking. The target is always in the camera field of view or slight occlusion (OCC). Compared with short-term tracking, long-term tracking is a more challenging task. It requires the ability to capture the target in long-term sequences and undergo frequent disappearances and reappearances of target. Therefore, long-term tracking is much closer to a realistic tracking system. However, few long-term tracking algorithms have been developed and few promising performances have been shown until now. We focus on a long-term visual tracking framework based on parts correlation filters (CFS). Our long-term tracking framework is composed of a part-based short-term tracker and a re-detection module. First, multiple CFS have been applied to locate the target collaboratively and address the partial OCC issue. Second, our method updates the part adaptively based on its motion similarity and reliability score to retain its robustness. Third, a switching strategy has been designed to dynamically activate the re-detection module and interact the search mode between local and global search. In addition, our re-detector is trained by sampling positive and negative samples around the reliable tracking target to adapt to the appearance changes. To evaluate the candidates from the re-detection module, verification has been carried out, which could ensure the precision of recovery. Numerous experimental results demonstrate that our proposed tracking method performs favorably against state-of-the-art methods in terms of accuracy and robustness.