Recently, trackers composed of a target estimation module and a target classification module have presented excellent accuracy with high efficiency. However, they underperform when encountering background semantic interference, large-scale variation, and long-term tracking. To address these problems, we propose a two-stage tracking framework. First, we propose a more applicable objective function for tracking tasks named metrizable intersection over union by considering the alignment mode and the center distance between two bounding boxes. Second, multilevel features are used to eliminate the semantic ambiguity by exploring diverse semantic information. Third, a meta-synthetic decision strategy is proposed to determine the optimum location of the target. In comprehensive experiments on OTB100, Lasot, TrackingNet, TColor-128, UAV123, and UAV20L, our method performs favorably against state-of-the-art trackers.