The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, most existing methods have not been done and their performances have also been limited. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Discriminative Correlation Filters (DCF) tracking algorithm with a re-detection component based on the SVM model. The DCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 dataset, the experimental results demonstrate the effectiveness of our algorithm in long-term tracking.