  Object tracking can be formulated as "finding the right object in a video".
We observe that recent approaches for class-agnostic tracking tend to focus on
the "finding" part, but largely overlook the "object" part of the task,
essentially doing a template matching over a frame in a sliding-window. In
contrast, class-specific trackers heavily rely on object priors in the form of
category-specific object detectors. In this work, we re-purpose
category-specific appearance models into a generic objectness prior. Our
approach converts a category-specific object detector into a category-agnostic,
object-specific detector (i.e. a tracker) efficiently, on the fly. Moreover, at
test time the same network can be applied to detection and tracking, resulting
in a unified approach for the two tasks. We achieve state-of-the-art results on
two recent large-scale tracking benchmarks (OxUvA and GOT, using external
data). By simply adding a mask prediction branch, our approach is able to
produce instance segmentation masks for the tracked object. Despite only using
box-level information on the first frame, our method outputs high-quality
masks, as evaluated on the DAVIS '17 video object segmentation benchmark.
