ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24
This addresses the problem of costly video annotation and system complexity for maritime computer vision applications, though it appears incremental as it builds on existing self-supervised and detection methods.
The paper tackles Multi-Object Tracking in maritime scenarios using UAVs and USVs by proposing an unsupervised method that uses self-supervised learning on ImageNet and high-quality detectors, achieving top 3 performance on UAV and USV benchmarks and winning championships in competitions like BDD100K MOT.
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