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

TL;DR

This paper presents an unsupervised multi-object tracking approach for maritime UAVs and USVs that leverages self-supervised instance representation learning, achieving high performance without complex association strategies or costly annotations.

Contribution

It introduces a novel unsupervised multi-object tracking method that simplifies the tracking process and reduces reliance on annotated data, outperforming existing methods in maritime scenarios.

Findings

Achieved top 3 performance on UAV and USV multi-object tracking benchmarks.

Won multiple MOT competitions including BDD100K MOT, MOTS, and Waymo 2D MOT.

Eliminated the need for complex association strategies and extensive annotated training data.

Abstract

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