  By decomposing the visual tracking task into two subproblems as
classification for pixel category and regression for object bounding box at
this pixel, we propose a novel fully convolutional Siamese network to solve
visual tracking end-to-end in a per-pixel manner. The proposed framework
SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature
extraction and one classification-regression subnetwork for bounding box
prediction. Our framework takes ResNet-50 as backbone. Different from
state-of-the-art trackers like Siamese-RPN, SiamRPN++ and SPM, which are based
on region proposal, the proposed framework is both proposal and anchor free.
Consequently, we are able to avoid the tricky hyper-parameter tuning of anchors
and reduce human intervention. The proposed framework is simple, neat and
effective. Extensive experiments and comparisons with state-of-the-art trackers
are conducted on many challenging benchmarks like GOT-10K, LaSOT, UAV123 and
OTB-50. Without bells and whistles, our SiamCAR achieves the leading
performance with a considerable real-time speed.
