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。