  Visual object tracking aims to estimate the location of an arbitrary target
in a video sequence given its initial bounding box. By utilizing offline
feature learning, the siamese paradigm has recently been the leading framework
for high performance tracking. However, current existing siamese trackers
either heavily rely on complicated anchor-based detection networks or lack the
ability to resist to distractors. In this paper, we propose the Siamese
keypoint prediction network (SiamKPN) to address these challenges. Upon a
Siamese backbone for feature embedding, SiamKPN benefits from a cascade heatmap
strategy for coarse-to-fine prediction modeling. In particular, the strategy is
implemented by sequentially shrinking the coverage of the label heatmap along
the cascade to apply loose-to-strict intermediate supervisions. During
inference, we find the predicted heatmaps of successive stages to be gradually
concentrated to the target and reduced to the distractors. SiamKPN performs
well against state-of-the-art trackers for visual object tracking on four
benchmark datasets including OTB-100, VOT2018, LaSOT and GOT-10k, while running
at real-time speed.
