  We present SDTracker, a method that harnesses the potential of synthetic data
for multi-object tracking of real-world scenes in a domain generalization and
semi-supervised fashion. First, we use the ImageNet dataset as an auxiliary to
randomize the style of synthetic data. With out-of-domain data, we further
enforce pyramid consistency loss across different "stylized" images from the
same sample to learn domain invariant features. Second, we adopt the
pseudo-labeling method to effectively utilize the unlabeled MOT17 training
data. To obtain high-quality pseudo-labels, we apply proximal policy
optimization (PPO2) algorithm to search confidence thresholds for each
sequence. When using the unlabeled MOT17 training set, combined with the
pure-motion tracking strategy upgraded via developed post-processing, we
finally reach 61.4 HOTA.
