Multi-object tracking algorithms have made significant advancements due to the recent developments in object detection. However, most existing methods primarily focus on tracking pedestrians or vehicles, which exhibit relatively simple and regular motion patterns. Consequently, there is a scarcity of algorithms that address the tracking of targets with irregular or non-linear motion, such as multi-athlete tracking. Furthermore, popular tracking algorithms often rely on the Kalman filter for object motion modeling, which fails to track objects when their motion contradicts the linear motion assumption of the Kalman filter. Due to this reason, we proposed a novel online and robust multi-object tracking approach, named Iterative Scale-Up ExpansionIoU and Deep Features for multi-object tracking. Unlike conventional methods, we abandon the use of the Kalman filter and propose utilizing the iterative scale-up expansion IoU. This approach achieves superior tracking performance without requiring additional training data or adopting a more robust detector, all while maintaining a lower computational cost compared to other appearance-based methods. Our proposed method demonstrates remarkable effectiveness in tracking irregular motion objects, achieving a score of 75.3% in HOTA. It outperforms all state-of-the-art online tracking algorithms on the SportsMOT dataset, covering various kinds of sport scenarios.

翻译：多目标跟踪算法因目标检测技术的近期发展而取得了显著进展。然而，现有方法主要聚焦于跟踪运动模式相对简单规则的 pedestrians 或车辆，因而缺乏针对不规则或非线性运动目标（如多运动员跟踪）的算法。此外，主流跟踪算法常依赖卡尔曼滤波器进行目标运动建模，当目标运动违背卡尔曼滤波的线性运动假设时，此类方法会失效。为此，我们提出了一种新颖的在线鲁棒多目标跟踪方法——迭代扩增扩展IoU与深度特征关联（Iterative Scale-Up ExpansionIoU and Deep Features for Multi-Object Tracking）。与常规方法不同，我们摒弃了卡尔曼滤波器，转而提出利用迭代扩增扩展IoU。该方法无需额外训练数据或采用更鲁棒的检测器，即可实现优越的跟踪性能，且计算成本低于其他基于外观的方法。我们的方法在跟踪不规则运动目标方面展现出显著有效性，在HOTA指标上达到75.3%的得分，并在覆盖多种体育场景的SportsMOT数据集上优于所有现有在线跟踪算法。