Despite the considerable advances that are emerged in correlation filter-based tracking, in fact, they may achieve excellent performance in robustness, speed, and accuracy; they still fail when dealing with large-scale alteration and show the inability to handle long-term tracking in complex scenarios, where the object undergoes partial occlusion, out-of-view, and deformation. In this paper, we propose a robust approach to address two important problems: the first one is scale estimation in kernelized correlation filter (KCF), and the second one is how to update the model in the process of tracking. We aim in this work to overcome the scale fixed size limitation of kernelized correlation filter-based tracking algorithms and improve the mechanism of model online training. Our approach learns a separate correlation filter to estimate the accurate target scale by finding the scale's candidate that maximizes the output response of the correlation filter mentioned above. Besides, we define a minimum rate of similarity for the online model update to avoid training with failure detections. Our approach is evaluated in terms of precision and accuracy, on a commonly used tracking benchmark with 100 challenging videos; the experimental results show that our proposed tracker outperforms the KCF algorithm and shows promising performance compared to state-of-the-art tracking methods.

尽管在基于相关滤波器的跟踪中出现了相当大的进步，但事实上，它们可能在鲁棒性、速度和准确性方面取得出色的性能；它们在处理大规模改变时仍然失败，并且在复杂场景中无法处理长期跟踪，其中对象经历部分遮挡、视野外和变形。在本文中，我们提出了一种稳健的方法来解决两个重要问题：第一个是核相关滤波器（KCF）中的尺度估计，第二个是如何在跟踪过程中更新模型。我们的目标是克服基于核相关滤波器的跟踪算法的规模固定大小限制，并改进模型在线训练的机制。我们的方法学习一个单独的相关滤波器，通过找到最大化上述相关滤波器的输出响应的尺度候选来估计准确的目标尺度。此外，我们定义了在线模型更新的最小相似率，以避免使用故障检测进行训练。我们的方法在精度和准确度方面进行了评估，在具有 100 个具有挑战性的视频的常用跟踪基准上进行了评估；实验结果表明，我们提出的跟踪器优于 KCF 算法，并且与最先进的跟踪方法相比显示出有希望的性能。我们的方法在精度和准确度方面进行了评估，在具有 100 个具有挑战性的视频的常用跟踪基准上进行了评估；实验结果表明，我们提出的跟踪器优于 KCF 算法，并且与最先进的跟踪方法相比显示出有希望的性能。我们的方法在精度和准确度方面进行了评估，在具有 100 个具有挑战性的视频的常用跟踪基准上进行了评估；实验结果表明，我们提出的跟踪器优于 KCF 算法，并且与最先进的跟踪方法相比显示出有希望的性能。