Abstract
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.