  Online tracking of multiple objects in videos requires strong capacity of
modeling and matching object appearances. Previous methods for learning
appearance embedding mostly rely on instance-level matching without considering
the temporal continuity provided by videos. We design a new instance-to-track
matching objective to learn appearance embedding that compares a candidate
detection to the embedding of the tracks persisted in the tracker. It enables
us to learn not only from videos labeled with complete tracks, but also
unlabeled or partially labeled videos. We implement this learning objective in
a unified form following the spirit of constrastive loss. Experiments on
multiple object tracking datasets demonstrate that our method can effectively
learning discriminative appearance embeddings in a semi-supervised fashion and
outperform state of the art methods on representative benchmarks.
