  Object tracking is divided into single-object tracking (SOT) and multi-object
tracking (MOT). MOT aims to maintain the identities of multiple objects across
a series of continuous video sequences. In recent years, MOT has made rapid
progress. However, modeling the motion and appearance models of objects in
complex scenes still faces various challenging issues. In this paper, we design
a novel direction consistency method for smooth trajectory prediction (STP-DC)
to increase the modeling of motion information and overcome the lack of
robustness in previous methods in complex scenes. Existing methods use
pedestrian re-identification (Re-ID) to model appearance, however, they extract
more background information which lacks discriminability in occlusion and
crowded scenes. We propose a hyper-grain feature embedding network (HG-FEN) to
enhance the modeling of appearance models, thus generating robust appearance
descriptors. We also proposed other robustness techniques, including CF-ECM for
storing robust appearance information and SK-AS for improving association
accuracy. To achieve state-of-the-art performance in MOT, we propose a robust
tracker named Rt-track, incorporating various tricks and techniques. It
achieves 79.5 MOTA, 76.0 IDF1 and 62.1 HOTA on the test set of MOT17.Rt-track
also achieves 77.9 MOTA, 78.4 IDF1 and 63.3 HOTA on MOT20, surpassing all
published methods.
