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.

翻译：多目标跟踪旨在跨视频帧估计物体的边界框和身份标识。检测框作为二维和三维多目标跟踪的基础，其检测分数的不可避免变化会导致跟踪后的物体缺失。我们提出了一种分层数据关联策略，用于挖掘低分检测框中的真实物体，从而缓解物体缺失和轨迹碎片化问题。该简单且通用的数据关联策略在二维和三维场景下均表现出有效性。在三维场景中，跟踪器更容易在世界坐标系中预测物体速度。我们提出了一种互补运动预测策略，将检测到的速度与卡尔曼滤波器相结合，以应对突然运动与短期消失问题。ByteTrackV2在nuScenes三维多目标跟踪排行榜的相机模态（AMOTA达56.4%）和激光雷达模态（AMOTA达70.1%）中均处于领先地位。此外，该算法无需参数且可与多种检测器集成，使其在实际应用中具有吸引力。源代码已发布于https://github.com/ifzhang/ByteTrack-V2。