In this paper, we present a new tracking architecture with an encoder-decoder
transformer as the key component. The encoder models the global spatio-temporal
feature dependencies between target objects and search regions, while the
decoder learns a query embedding to predict the spatial positions of the target
objects. Our method casts object tracking as a direct bounding box prediction
problem, without using any proposals or predefined anchors. With the
encoder-decoder transformer, the prediction of objects just uses a simple
fully-convolutional network, which estimates the corners of objects directly.
The whole method is end-to-end, does not need any postprocessing steps such as
cosine window and bounding box smoothing, thus largely simplifying existing
tracking pipelines. The proposed tracker achieves state-of-the-art performance
on five challenging short-term and long-term benchmarks, while running at
real-time speed, being 6x faster than Siam R-CNN. Code and models are
open-sourced at this https URL

本文提出一种新的追踪架构，它以编码 - 解码变压器作为核心组件，将目标追踪转化为直接边界框预测问题，从而实现了端到端模型，无需使用任何先前设定的锚点或提案等后处理步骤。