Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional Intersection over Union (IoU) distance measure. By leveraging projected probability distributions on the ground plane, our approach efficiently captures motion patterns and adeptly manages uncertainties introduced by homography projections. Remarkably, UCMCTrack, relying solely on motion cues, achieves state-of-the-art performance across a variety of challenging datasets, including MOT17, MOT20, DanceTrack and KITTI. More details and code are available at https://github.com/corfyi/UCMCTrack

翻译：视频序列中的多目标跟踪（MOT）仍是一项具有挑战性的任务，尤其是在存在显著相机运动的情况下。这是因为目标可能在图像平面上发生大幅漂移，导致错误的跟踪结果。应对此类挑战通常需要辅助外观线索或相机运动补偿（CMC）。尽管这些策略有效，但它们也引入了显著的计算负担，给实时MOT带来了挑战。为此，我们提出UCMCTrack，一种对相机运动具有鲁棒性的新型运动模型跟踪器。与逐帧计算补偿参数的传统CMC不同，UCMCTrack在整个视频序列中始终应用相同的补偿参数。它在地平面上使用卡尔曼滤波器，并引入映射马氏距离（MMD）作为传统交并比（IoU）距离度量的替代方案。通过利用地平面上的投影概率分布，我们的方法能够高效捕获运动模式，并巧妙处理由单应性投影引入的不确定性。值得注意的是，仅依赖运动线索的UCMCTrack在包括MOT17、MOT20、DanceTrack和KITTI在内的多个具有挑战性的数据集上均取得了最先进的性能。更多详情和代码请访问https://github.com/corfyi/UCMCTrack