Siamese network-based trackers have shown remarkable success in aerial
tracking. Most previous works, however, usually perform template matching only
between the initial template and the search region and thus fail to deal with
rapidly changing targets that often appear in aerial tracking. As a remedy,
this work presents Building Appearance Collection Tracking (BACTrack). This
simple yet effective tracking framework builds a dynamic collection of target
templates online and performs efficient multi-template matching to achieve
robust tracking. Specifically, BACTrack mainly comprises a Mixed-Temporal
Transformer (MTT) and an appearance discriminator. The former is responsible
for efficiently building relationships between the search region and multiple
target templates in parallel through a mixed-temporal attention mechanism. At
the same time, the appearance discriminator employs an online adaptive
template-update strategy to ensure that the collected multiple templates remain
reliable and diverse, allowing them to closely follow rapid changes in the
target's appearance and suppress background interference during tracking.
Extensive experiments show that our BACTrack achieves top performance on four
challenging aerial tracking benchmarks while maintaining an impressive speed of
over 87 FPS on a single GPU. Speed tests on embedded platforms also validate
our potential suitability for deployment on UAV platforms.

Siamese network-based trackers have been successful in aerial tracking, but this work presents BACTrack, a tracking framework that builds a dynamic collection of target templates online and performs efficient multi-template matching, achieving robust tracking with top performance on aerial tracking benchmarks and high speed on GPU and embedded platforms.

BACTrack: 建筑外观收集用于航空追踪

BACTrack: Building Appearance Collection for Aerial Tracking

Temporal contexts among consecutive frames are far from being fully utilized
in existing visual trackers. In this work, we present TCTrack, a comprehensive
framework to fully exploit temporal contexts for aerial tracking. The temporal
contexts are incorporated at \textbf{two levels}: the extraction of
\textbf{features} and the refinement of \textbf{similarity maps}. Specifically,
for feature extraction, an online temporally adaptive convolution is proposed
to enhance the spatial features using temporal information, which is achieved
by dynamically calibrating the convolution weights according to the previous
frames. For similarity map refinement, we propose an adaptive temporal
transformer, which first effectively encodes temporal knowledge in a
memory-efficient way, before the temporal knowledge is decoded for accurate
adjustment of the similarity map. TCTrack is effective and efficient:
evaluation on four aerial tracking benchmarks shows its impressive performance;
real-world UAV tests show its high speed of over 27 FPS on NVIDIA Jetson AGX
Xavier.

本文提出了一种全面利用时间上下文进行航空追踪的框架，名为 TCTrack，该框架在特征提取和相似度图优化方面使用了时间上下文。实验表明，TCTrack 在四个航空追踪基准测试数据集上的效果令人印象深刻，并且在 NVIDIA Jetson AGX Xavier 上实现了高达 27 FPS 的高速。

TCTrack: 航空跟踪的时间上下文

TCTrack: Temporal Contexts for Aerial Tracking

In the domain of visual tracking, most deep learning-based trackers highlight
the accuracy but casting aside efficiency. Therefore, their real-world
deployment on mobile platforms like the unmanned aerial vehicle (UAV) is
impeded. In this work, a novel two-stage Siamese network-based method is
proposed for aerial tracking, \textit{i.e.}, stage-1 for high-quality anchor
proposal generation, stage-2 for refining the anchor proposal. Different from
anchor-based methods with numerous pre-defined fixed-sized anchors, our
no-prior method can 1) increase the robustness and generalization to different
objects with various sizes, especially to small, occluded, and fast-moving
objects, under complex scenarios in light of the adaptive anchor generation, 2)
make calculation feasible due to the substantial decrease of anchor numbers. In
addition, compared to anchor-free methods, our framework has better performance
owing to refinement at stage-2. Comprehensive experiments on three benchmarks
have proven the superior performance of our approach, with a speed of around
200 frames/s.

该研究提出了一种新的基于 Siamese 网络的两阶段方法，即第一阶段用于高质量锚点提取，第二阶段用于锚点的细化优化，从而实现了对小物体、遮挡物和快速移动物体的自适应锚点生成，大大降低了计算量，在三个基准测试中获得了更优秀的表现，并取得了大约 200 帧 / 秒的速度。