  The critical challenge in tracking-by-detection framework is how to avoid
drift problem during online learning, where the robust features for a variety
of appearance changes are difficult to be learned and a reasonable intersection
over union (IoU) threshold that defines the true/false positives is hard to
set. This paper presents the TCDCaps method to address the problems above via a
cascaded dense capsule architecture. To get robust features, we extend original
capsules with dense-connected routing, which are referred as DCaps. Depending
on the preservation of part-whole relationships in the Capsule Networks, our
dense-connected capsules can capture a variety of appearance variations. In
addition, to handle the issue of IoU threshold, a cascaded DCaps model (CDCaps)
is proposed to improve the quality of candidates, it consists of sequential
DCaps trained with increasing IoU thresholds so as to sequentially improve the
quality of candidates. Extensive experiments on 3 popular benchmarks
demonstrate the robustness of the proposed TCDCaps.
