  In this paper, SIA_Track is presented which is developed by a research team
from SI Analytics. The proposed method was built from pre-existing detector and
tracker under the tracking-by-detection paradigm. The tracker we used is an
online tracker that merely links newly received detections with existing
tracks. The core part of our method is training procedure of the object
detector where synthetic and unlabeled real data were only used for training.
To maximize the performance on real data, we first propose to use
pseudo-labeling that generates imperfect labels for real data using a model
trained with synthetic dataset. After that model soups scheme was applied to
aggregate weights produced during iterative pseudo-labeling. Besides,
cross-domain mixed sampling also helped to increase detection performance on
real data. Our method, SIA_Track, takes the first place on MOTSynth2MOT17 track
at BMTT 2022 challenge. The code is available on
https://github.com/SIAnalytics/BMTT2022_SIA_track.
