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.

提出了一种新的多目标跟踪算法 ISU-Net, 其使用了迭代放大扩展 IoU 算法，能够有效跟踪非线性运动的目标，并相比其他算法，具有更低的计算成本