  Multi-Object Tracking is one of the most important technologies in maritime
computer vision. Our solution tries to explore Multi-Object Tracking in
maritime Unmanned Aerial vehicles (UAVs) and Unmanned Surface Vehicles (USVs)
usage scenarios. Most of the current Multi-Object Tracking algorithms require
complex association strategies and association information (2D location and
motion, 3D motion, 3D depth, 2D appearance) to achieve better performance,
which makes the entire tracking system extremely complex and heavy. At the same
time, most of the current Multi-Object Tracking algorithms still require video
annotation data which is costly to obtain for training. Our solution tries to
explore Multi-Object Tracking in a completely unsupervised way. The scheme
accomplishes instance representation learning by using self-supervision on
ImageNet. Then, by cooperating with high-quality detectors, the multi-target
tracking task can be completed simply and efficiently. The scheme achieved top
3 performance on both UAV-based Multi-Object Tracking with Reidentification and
USV-based Multi-Object Tracking benchmarks and the solution won the
championship in many multiple Multi-Object Tracking competitions. such as
BDD100K MOT,MOTS, Waymo 2D MOT
