Existing deep trackers mainly use convolutional neural networks pre-trained
for generic object recognition task for representations. Despite demonstrated
successes for numerous vision tasks, the contributions of using pre-trained
deep features for visual tracking are not as significant as that for object
recognition. The key issue is that in visual tracking the targets of interest
can be arbitrary object class with arbitrary forms. As such, pre-trained deep
features are less effective in modeling these targets of arbitrary forms for
distinguishing them from the background. In this paper, we propose a novel
scheme to learn target-aware features, which can better recognize the targets
undergoing significant appearance variations than pre-trained deep features. To
this end, we develop a regression loss and a ranking loss to guide the
generation of target-active and scale-sensitive features. We identify the
importance of each convolutional filter according to the back-propagated
gradients and select the target-aware features based on activations for
representing the targets. The target-aware features are integrated with a
Siamese matching network for visual tracking. Extensive experimental results
show that the proposed algorithm performs favorably against the
state-of-the-art methods in terms of accuracy and speed.

本文提出一种新颖的方案来学习目标感知特征，用于视觉跟踪，并将目标感知特征与孪生匹配网络相结合，实现与最先进技术相比精度和速度均有优异表现。

目标感知深度追踪

Target-Aware Deep Tracking

In this paper, we propose a novel on-line visual tracking framework based on
the Siamese matching network and meta-learner network, which run at real-time
speeds. Conventional deep convolutional feature-based discriminative visual
tracking algorithms require continuous re-training of classifiers or
correlation filters, which involve solving complex optimization tasks to adapt
to the new appearance of a target object. To alleviate this complex process,
our proposed algorithm incorporates and utilizes a meta-learner network to
provide the matching network with new appearance information of the target
objects by adding target-aware feature space. The parameters for the
target-specific feature space are provided instantly from a single forward-pass
of the meta-learner network. By eliminating the necessity of continuously
solving complex optimization tasks in the course of tracking, experimental
results demonstrate that our algorithm performs at a real-time speed while
maintaining competitive performance among other state-of-the-art tracking
algorithms.

提出了一种基于 Siamese 匹配网络和元学习网络的新型在线视觉跟踪框架，其运行速度非常快。该算法利用元学习网络提供目标对象的新外观信息，通过添加目标感知特征空间，消除了在跟踪中不断解决复杂优化任务的必要性。在保持与其他最先进的跟踪算法相当的性能的同时，实验结果证明我们的算法运行速度是实时的。