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

ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box