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 76.9% in HOTA. It outperforms all state-of-the-art tracking algorithms on the SportsMOT dataset, covering various kinds of sport scenarios.

翻译：多目标追踪算法因目标检测技术的近期发展而取得了显著进步。然而，现有方法主要聚焦于追踪具有相对简单规律运动模式的行人或车辆。因此，针对非线性不规则运动目标（如多运动员追踪）的算法较为匮乏。此外，主流追踪算法常依赖卡尔曼滤波进行目标运动建模，当目标运动违背该滤波器的线性运动假设时，追踪将失效。针对这一局限，我们提出一种新颖的在线鲁棒多目标追踪方法——迭代尺度扩展IoU与深度特征关联算法。与常规方法不同，本工作摒弃卡尔曼滤波，创新性地采用迭代尺度扩展IoU。该方案无需额外训练数据或更鲁棒的检测器即可实现卓越追踪性能，且计算成本低于其他基于外观特征的方法。本方法在追踪不规则运动目标时展现出显著有效性，在HOTA指标上达到76.9%的得分。涵盖多种运动场景的SportsMOT数据集上，本方法全面超越所有最先进的追踪算法。