Visual object tracking acts as a pivotal component in various emerging video
applications. Despite the numerous developments in visual tracking, existing
deep trackers are still likely to fail when tracking against objects with
dramatic variation. These deep trackers usually do not perform online update or
update single sub-branch of the tracking model, for which they cannot adapt to
the appearance variation of objects. Efficient updating methods are therefore
crucial for tracking while previous meta-updater optimizes trackers directly
over parameter space, which is prone to over-fit even collapse on longer
sequences. To address these issues, we propose a context-aware tracking model
to optimize the tracker over the representation space, which jointly
meta-update both branches by exploiting information along the whole sequence,
such that it can avoid the over-fitting problem. First, we note that the
embedded features of the localization branch and the box-estimation branch,
focusing on the local and global information of the target, are effective
complements to each other. Based on this insight, we devise a
context-aggregation module to fuse information in historical frames, followed
by a context-aware module to learn affinity vectors for both branches of the
tracker. Besides, we develop a dedicated meta-learning scheme, on account of
fast and stable updating with limited training samples. The proposed tracking
method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS,
demonstrating its capability of improving the accuracy and robustness of the
underlying tracker with little speed drop.

在本文中，我们提出了一种基于上下文感知的跟踪模型，该模型是通过利用整个序列中的信息联合元更新两个分支来优化表示空间上的跟踪器，实现对外观变化的自适应。我们采用上下文聚合模块融合历史帧中的信息，然后使用上下文感知模块为跟踪器的两个分支学习亲和向量。通过使用专用的元学习方案，在有限的训练样本下，该跟踪方法在 VOT2018 上实现了 0.514 的 EAO 评分，40FPS 的速度，表明了它提高了潜在跟踪器的准确性和鲁棒性，并且在速度上有小幅下降。