记录一个自己翻译的中文版本,可以反复观看。 论文: https://arxiv.org/abs/2205.11761代码: https://github.com/sansanfree/RBO摘要目前基于孪生的跟踪器主要将视觉跟踪分成两个独立的子任务,包括分类和定位。…
本文探讨了一种名为基于排序的优化(Ranking-Based Optimization, RBO)的视觉跟踪算法,旨在解决基于孪生网络的跟踪器在区分背景干扰、定位预测与分类预测不一致等问题。RBO算法引入了两种排序损失:分类排序损失和IoU引导排序损失,旨在增强跟踪器的鲁棒性和定位准确性。具体而言,分类排序损失用于优化正样本与难...
The fast tracking speed of Siamese-based RGB-T tracking has garnered significant attention. However, current Siamese-based RGB-T trackers still face certain limitations, including insufficient bounding box estimation, neglecting the interaction between positive and negative samples, and the complexity asso...
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Overall, compared to RPN-based algorithms, anchor-free tracking algorithms could reduce the computational burden and, to some extent, reduce computational time. Anchor-free Siamese object tracking is a trend for the future. 2.2. Attention Mechanism An important module in deep learning is the ...
Trackers based on the Siamese network have received much attention in recent years, owing to its remarkable performance, and the task of object tracking is to predict the location of the target in current frame. However, during the tracking process, dist