  Multi-object tracking (MOT) aims at estimating bounding boxes and identities
of objects across video frames. Detection boxes serve as the basis of both 2D
and 3D MOT. The inevitable changing of detection scores leads to object missing
after tracking. We propose a hierarchical data association strategy to mine the
true objects in low-score detection boxes, which alleviates the problems of
object missing and fragmented trajectories. The simple and generic data
association strategy shows effectiveness under both 2D and 3D settings. In 3D
scenarios, it is much easier for the tracker to predict object velocities in
the world coordinate. We propose a complementary motion prediction strategy
that incorporates the detected velocities with a Kalman filter to address the
problem of abrupt motion and short-term disappearing. ByteTrackV2 leads the
nuScenes 3D MOT leaderboard in both camera (56.4% AMOTA) and LiDAR (70.1%
AMOTA) modalities. Furthermore, it is nonparametric and can be integrated with
various detectors, making it appealing in real applications. The source code is
released at https://github.com/ifzhang/ByteTrack-V2.
