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Multi-Object Tracking is one of the most important technologies in maritimecomputer vision. Our solution tries to explore Multi-Object Tracking inmaritime Unmanned Aerial vehicles (UAVs) and Unmanned Surface Vehicles (USVs)usage scenarios. Most of the current Multi-Object Tracking algorithms requirecomplex association strategies and association information (2D location andmotion, 3D motion, 3D depth, 2D appearance) to achieve better performance,which makes the entire tracking system extremely complex and heavy. At the sametime, most of the current Multi-Object Tracking algorithms still require videoannotation data which is costly to obtain for training. Our solution tries toexplore Multi-Object Tracking in a completely unsupervised way. The schemeaccomplishes instance representation learning by using self-supervision onImageNet. Then, by cooperating with high-quality detectors, the multi-targettracking task can be completed simply and efficiently. The scheme achieved top3 performance on both UAV-based Multi-Object Tracking with Reidentification andUSV-based Multi-Object Tracking benchmarks and the solution won thechampionship in many multiple Multi-Object Tracking competitions. such asBDD100K MOT,MOTS, Waymo 2D MOT

ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24