Region proposal networks (RPN) have been recently combined with the Siamese
network for tracking, and shown excellent accuracy with high efficiency.
Nevertheless, previously proposed one-stage Siamese-RPN trackers degenerate in
presence of similar distractors and large scale variation. Addressing these
issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN
(C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to
shallow low-level layers in a Siamese network. Compared to previous solutions,
C-RPN has several advantages: (1) Each RPN is trained using the outputs of RPN
in the previous stage. Such process stimulates hard negative sampling,
resulting in more balanced training samples. Consequently, the RPNs are
sequentially more discriminative in distinguishing difficult background (i.e.,
similar distractors). (2) Multi-level features are fully leveraged through a
novel feature transfer block (FTB) for each RPN, further improving the
discriminability of C-RPN using both high-level semantic and low-level spatial
information. (3) With multiple steps of regressions, C-RPN progressively
refines the location and shape of the target in each RPN with adjusted anchor
boxes in the previous stage, which makes localization more accurate. C-RPN is
trained end-to-end with the multi-task loss function. In inference, C-RPN is
deployed as it is, without any temporal adaption, for real-time tracking. In
extensive experiments on OTB-2013, OTB-2015, VOT-2016, VOT-2017, LaSOT and
TrackingNet, C-RPN consistently achieves state-of-the-art results and runs in
real-time.

本文提出了一种基于级联区域建议网络和 Siamese 跟踪的多级跟踪框架，其中每个区域建议网络利用前一阶段的区域建议网络的输出进行训练，采用多任务损失函数进行端到端的训练，并在多个数据集上实现了优异的跟踪效果。