  Visual object tracking is an important application of computer vision.
Recently, Siamese based trackers have achieved good accuracy. However, most of
Siamese based trackers are not efficient, as they exhaustively search potential
object locations to define anchors and then classify each anchor (i.e., a
bounding box). This paper develops the first Anchor Free Siamese Network
(AFSN). Specifically, a target object is defined by a bounding box center,
tracking offset, and object size. All three are regressed by Siamese network
with no additional classification or regional proposal, and performed once for
each frame. We also tune the stride and receptive field for Siamese network,
and further perform ablation experiments to quantitatively illustrate the
effectiveness of our AFSN. We evaluate AFSN using five most commonly used
benchmarks and compare to the best anchor-based trackers with source codes
available for each benchmark. AFSN is 3-425 times faster than these best anchor
based trackers. AFSN is also 5.97% to 12.4% more accurate in terms of all
metrics for benchmark sets OTB2015, VOT2015, VOT2016, VOT2018 and TrackingNet,
except that SiamRPN++ is 4% better than AFSN in terms of Expected Average
Overlap (EAO) on VOT2018 (but SiamRPN++ is 3.9 times slower).
