Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive discriminative correlation filters (LADCF) tracking algorithm with a re-detection component based on the support vector machine (SVM) model. The LADCF 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 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

长期视觉跟踪比短期跟踪面临更多挑战，并且更接近于实际应用。但是，大多数现有方法的性能在长期跟踪任务中受到限制。在这项工作中，我们提出了一种可靠而简单的长期跟踪方法，该方法将基于支持向量机（SVM）的重新检测组件扩展了最新的学习自适应判别相关滤波器（LADCF）跟踪算法）模型。LADCF跟踪算法将目标定位在每个帧中，并且当跟踪失败时，重新检测器能够有效地重新检测整个图像中的目标。我们进一步介绍了一种鲁棒的置信度评估标准，该标准结合了最大响应标准和平均峰相关能量（APCE）来判断预测目标的置信度。当置信度通常较高时，将相应地更新SVM。如果置信度急剧下降，则SVM将重新检测目标。我们对OTB-2015和UAV123数据集进行了广泛的实验。实验结果证明了我们算法在长期跟踪中的有效性。