论文标题
基于排名的暹罗视觉跟踪
Ranking-Based Siamese Visual Tracking
论文作者
论文摘要
当前基于暹罗的跟踪器主要将视觉跟踪制定为两个独立的子任务，包括分类和本地化。他们通过分别处理每个样本并忽略正面和负样本之间的关系来学习分类子网。此外，这种跟踪范式仅对最终预测的建议的分类信心，这可能会产生分类和本地化之间的错位。为了解决这些问题，本文提出了一种基于排名的优化算法，以探讨不同建议之间的关系。为此，我们介绍了两个排名损失，包括分类一个和IOU指导的损失作为优化约束。分类排名损失可以确保积极样本的排名高于硬性损失，即干扰物，以便跟踪器可以成功选择前景样本而不会被干扰物欺骗。 IOU指导的排名损失旨在使分类置信分数与正面样本的相应定位预测的联合（IOU）相交，从而使良好定位的预测由高分类置信度表示。具体而言，提出的两个排名损失与大多数暹罗跟踪器兼容，并且没有进行推理的其他计算。对七个跟踪基准测试的广泛实验，包括OTB100，UAV123，TC128，DOCT2016，NFS30，GOT-10K和LASOT，证明了基于排名的优化算法的有效性。代码和原始结果可在https://github.com/sansanfree/rbo上获得。
Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence of proposals for the final prediction, which may yield the misalignment between classification and localization. To resolve these issues, this paper proposes a ranking-based optimization algorithm to explore the relationship among different proposals. To this end, we introduce two ranking losses, including the classification one and the IoU-guided one, as optimization constraints. The classification ranking loss can ensure that positive samples rank higher than hard negative ones, i.e., distractors, so that the trackers can select the foreground samples successfully without being fooled by the distractors. The IoU-guided ranking loss aims to align classification confidence scores with the Intersection over Union(IoU) of the corresponding localization prediction for positive samples, enabling the well-localized prediction to be represented by high classification confidence. Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference. Extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, NFS30, GOT-10k and LaSOT, demonstrate the effectiveness of the proposed ranking-based optimization algorithm. The code and raw results are available at https://github.com/sansanfree/RBO.
