Recently, Siamese network based trackers have received tremendous interest
for their fast tracking speed and high performance. Despite the great success,
this tracking framework still suffers from several limitations. First, it
cannot properly handle large object rotation. Second, tracking gets easily
distracted when the background contains salient objects. In this paper, we
propose two simple yet effective mechanisms, namely angle estimation and
spatial masking, to address these issues. The objective is to extract more
representative features so that a better match can be obtained between the same
object from different frames. The resulting tracker, named Siam-BM, not only
significantly improves the tracking performance, but more importantly maintains
the realtime capability. Evaluations on the VOT2017 dataset show that Siam-BM
achieves an EAO of 0.335, which makes it the best-performing realtime tracker
to date.

在本文中，我们提出了两种简单而有效的机制：角度估计和空间遮罩，以解决 Siamese 网络追踪器的旋转和背景干扰问题，并提出了一个名为 Siam-BM 的结果追踪器，它不仅显着提高了追踪性能，而且更重要的是保持了实时性。在 VOT2017 数据集上的评估表明，Siam-BM 实现了 0.335 的 EAO，这使其成为迄今为止表现最佳的实时追踪器。