  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
https://github.com/researchmm/TracKit.
