Transformer-based visual trackers have demonstrated significant progress
owing to their superior modeling capabilities. However, existing trackers are
hampered by low speed, limiting their applicability on devices with limited
computational power. To alleviate this problem, we propose HiT, a new family of
efficient tracking models that can run at high speed on different devices while
retaining high performance. The central idea of HiT is the Bridge Module, which
bridges the gap between modern lightweight transformers and the tracking
framework. The Bridge Module incorporates the high-level information of deep
features into the shallow large-resolution features. In this way, it produces
better features for the tracking head. We also propose a novel dual-image
position encoding technique that simultaneously encodes the position
information of both the search region and template images. The HiT model
achieves promising speed with competitive performance. For instance, it runs at
61 frames per second (fps) on the Nvidia Jetson AGX edge device. Furthermore,
HiT attains 64.6% AUC on the LaSOT benchmark, surpassing all previous efficient
trackers.

引入 HiT 模型和 Bridge Module 以及双图像位置编码技术，实现高速且性能优越的追踪模型。

探索轻量级分层视觉转换器用于高效视觉跟踪

Exploring Lightweight Hierarchical Vision Transformers for Efficient  Visual Tracking

One of the main challenges of visual object tracking comes from the arbitrary
appearance of objects. Most existing algorithms try to resolve this problem as
an object-specific task, i.e., the model is trained to regenerate or classify a
specific object. As a result, the model need to be initialized and retrained
for different objects. In this paper, we propose a more generic approach
utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN
takes two inputs (one is object image patch, the other is search image patch),
then outputs a response map which predicts how likely the object appears in a
specific location. Unlike those object-specific approach, the YCNN is trained
to measure the similarity between two image patches. Thus it will not be
confined to any specific object. Furthermore the network can be end-to-end
trained to extract both shallow and deep convolutional features which are
dedicated for visual tracking. And once properly trained, the YCNN can be
applied to track all kinds of objects without further training and updating.
Benefiting from the once-for-all model, our algorithm is able to run at a very
high speed of 45 frames-per-second. The experiments on 51 sequences also show
that our algorithm achieves an outstanding performance.

提出了一种基于卷积神经网络的视觉目标跟踪方法，使用两个输入，分别为目标图像补丁和搜索图像补丁，输出一个响应映射，该映射能够预测目标出现在特定位置的可能性，并且能够以非特定对象的方式进行培训，实现端到端的训练，同时拥有高速帧率和优异的表现。