A target tracking algorithm via confidence evaluation is proposed to add the scale and model updating method to the correlation filter. In the process of tracking, there are many cases of occlusion and similar interference. If the model parameter is continuously updated, it can easily lead to false track and target loss. Therefore, the quality of tracking is judged qualitatively by the confidence. When the confidence is low, we stop updating to prevent the introduction of error and improve the accuracy. After ensuring the tracking is correct, we can detect and update the scale size. We propose a faster scale updating method with redundant code simplified, and make the tracking more accurate and with lower time cost. The experimental results show that the proposed algorithm improves the precision and success rate by 38% and 33%, respectively compared to the original algorithm, it has better performance than several existing algorithms, and is more robust to cope with occlusion and scaling.