  The greatest challenge facing visual object tracking is the simultaneous
requirements on robustness and discrimination power. In this paper, we propose
a SiamFC-based tracker, named SPM-Tracker, to tackle this challenge. The basic
idea is to address the two requirements in two separate matching stages.
Robustness is strengthened in the coarse matching (CM) stage through
generalized training while discrimination power is enhanced in the fine
matching (FM) stage through a distance learning network. The two stages are
connected in series as the input proposals of the FM stage are generated by the
CM stage. They are also connected in parallel as the matching scores and box
location refinements are fused to generate the final results. This innovative
series-parallel structure takes advantage of both stages and results in
superior performance. The proposed SPM-Tracker, running at 120fps on GPU,
achieves an AUC of 0.687 on OTB-100 and an EAO of 0.434 on VOT-16, exceeding
other real-time trackers by a notable margin.
