  Joint detection and embedding (JDE) based methods usually estimate bounding
boxes and embedding features of objects with a single network in Multi-Object
Tracking (MOT). In the tracking stage, JDE-based methods fuse the target motion
information and appearance information by applying the same rule, which could
fail when the target is briefly lost or blocked. To overcome this problem, we
propose a new association matrix, the Embedding and Giou matrix, which combines
embedding cosine distance and Giou distance of objects. To further improve the
performance of data association, we develop a simple, effective tracker named
SimpleTrack, which designs a bottom-up fusion method for Re-identity and
proposes a new tracking strategy based on our EG matrix. The experimental
results indicate that SimpleTrack has powerful data association capability,
e.g., 61.6 HOTA and 76.3 IDF1 on MOT17. In addition, we apply the EG matrix to
5 different state-of-the-art JDE-based methods and achieve significant
improvements in IDF1, HOTA and IDsw metrics, and increase the tracking speed of
these methods by about 20%.
