Template-based discriminative trackers are currently the dominant tracking
methods due to their robustness and accuracy, and the Siamese-network-based
methods that depend on cross-correlation operation between features extracted
from template and search images show the state-of-the-art tracking performance.
However, general cross-correlation operation can only obtain relationship
between local patches in two feature maps. In this paper, we propose a novel
tracker network based on a powerful attention mechanism called Transformer
encoder-decoder architecture to gain global and rich contextual
interdependencies. In this new architecture, features of the template image is
processed by a self-attention module in the encoder part to learn strong
context information, which is then sent to the decoder part to compute
cross-attention with the search image features processed by another
self-attention module. In addition, we design the classification and regression
heads using the output of Transformer to localize target based on
shape-agnostic anchor. We extensively evaluate our tracker TrTr, on VOT2018,
VOT2019, OTB-100, UAV, NfS, TrackingNet, and LaSOT benchmarks and our method
performs favorably against state-of-the-art algorithms. Training code and
pretrained models are available at this https URL

本文介绍了一种基于 Transformer 编码器 - 解码器架构和强有力的注意机制的新型追踪器网络，通过自我关注模块和交叉注意力，提高了全局和丰富的背景信息获取，支持超越局部特征的追踪效果，在多个基准测试上表现出优异的性能。