  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.
