Visual object tracking (VOT) based on discriminative correlation filters (DCF) has received great attention due to its higher computational efficiency and better robustness. However, DCF-based methods suffer from the problem of model contamination. The tracker will drift into the background due to the uncertainties brought by shifting among peaks, which will further lead to the issues of model degradation. To deal with occlusions, a novel Occlusion-Handling Tracker Based on Discriminative Correlation Filters (OHDCF) framework is proposed for online visual object tracking, where an occlusion-handling strategy is integrated into the spatial–temporal regularized correlation filters (STRCF). The occlusion-handling tracker follows a hybrid approach to handle partial occlusion and complete occlusion. Specifically, we first present a function to determine whether occlusion occurs. Then, the proposed filter uses block-based and feature-matching methods to determine whether an object is partially occluded or completely occluded. Following this, we use different methods to track the target. Extensive experiments have performed on OTB-100, Temple-Color-128, VOT-2016 and VOT-2018 datasets, the results show that OHDCF achieves promising performance compared to other state-of-the-art trackers. On VOT-2018, OHDCF significantly outperforms STRCF from the challenge with a relative gain of 4.8 in EAO and a gain of 4.6 in Accuracy.

中文翻译：

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基于判别相关滤波器的遮挡处理跟踪器

基于判别相关滤波器（DCF）的视觉对象跟踪（VOT）由于其更高的计算效率和更好的鲁棒性而受到了广泛的关注。但是，基于DCF的方法存在模型污染的问题。由于峰间移动带来的不确定性，跟踪器将漂移到背景中，这将进一步导致模型降级的问题。为了解决遮挡问题，提出了一种基于判别相关过滤器（OHDCF）的新颖遮挡处理跟踪器，用于在线视觉对象跟踪，该方法将遮挡处理策略集成到时空正则化相关过滤器（STRCF）中。遮挡处理跟踪器遵循一种混合方法来处理部分遮挡和完全遮挡。特别，我们首先提出确定是否发生闭塞的功能。然后，提出的过滤器使用基于块的和特征匹配的方法来确定对象是部分遮挡还是完全遮挡。此后，我们使用不同的方法来跟踪目标。在OTB-100，Temple-Color-128，VOT-2016和VOT-2018数据集上进行了广泛的实验，结果表明，与其他最新的跟踪器相比，OHDCF的性能令人鼓舞。在VOT-2018上，OHHDF的挑战相对于STRCF明显胜过STRCF，相对增益为4.8 在OTB-100，Temple-Color-128，VOT-2016和VOT-2018数据集上进行了广泛的实验，结果表明，与其他最新的跟踪器相比，OHDCF的性能令人鼓舞。在VOT-2018上，OHHDF的挑战相对于STRCF明显胜过STRCF，相对增益为4.8 在OTB-100，Temple-Color-128，VOT-2016和VOT-2018数据集上进行了广泛的实验，结果表明，与其他最新的跟踪器相比，OHDCF的性能令人鼓舞。在VOT-2018上，OHHDF的挑战相对于STRCF明显胜过STRCF，相对增益为4.8 在EAO中获得4.6 准确性。