Anchor-based Siamese trackers have achieved remarkable advancements in
accuracy, yet the further improvement is restricted by the lagged tracking
robustness. We find the underlying reason is that the regression network in
anchor-based methods is only trained on the positive anchor boxes (i.e., $IoU
\geq0.6$). This mechanism makes it difficult to refine the anchors whose
overlap with the target objects are small. In this paper, we propose a novel
object-aware anchor-free network to address this issue. First, instead of
refining the reference anchor boxes, we directly predict the position and scale
of target objects in an anchor-free fashion. Since each pixel in groundtruth
boxes is well trained, the tracker is capable of rectifying inexact predictions
of target objects during inference. Second, we introduce a feature alignment
module to learn an object-aware feature from predicted bounding boxes. The
object-aware feature can further contribute to the classification of target
objects and background. Moreover, we present a novel tracking framework based
on the anchor-free model. The experiments show that our anchor-free tracker
achieves state-of-the-art performance on five benchmarks, including VOT-2018,
VOT-2019, OTB-100, GOT-10k and LaSOT. The source code is available at
this https URL

该论文提出了一种基于物体感知的无锚点跟踪网络，通过直接预测目标对象的位置和规模，以及引入功能对齐模块来从预测的边界框中学习物体感知特征，从而提高跟踪鲁棒性和精度。实验证明，该跟踪器在五个基准测试中均取得了最先进的性能。