In the field of multi-object tracking (MOT), traditional methods often rely
on the Kalman Filter for motion prediction, leveraging its strengths in linear
motion scenarios. However, the inherent limitations of these methods become
evident when confronted with complex, nonlinear motions and occlusions
prevalent in dynamic environments like sports and dance. This paper explores
the possibilities of replacing the Kalman Filter with various learning-based
motion model that effectively enhances tracking accuracy and adaptability
beyond the constraints of Kalman Filter-based systems. In this paper, we
proposed MambaTrack, an online motion-based tracker that outperforms all
existing motion-based trackers on the challenging DanceTrack and SportsMOT
datasets. Moreover, we further exploit the potential of the state-space-model
in trajectory feature extraction to boost the tracking performance and proposed
MambaTrack+, which achieves the state-of-the-art performance on DanceTrack
dataset with 56.1 HOTA and 54.9 IDF1.

本文研究了多目标跟踪领域中使用 Kalman 滤波器的传统方法在面对复杂非线性运动和遮挡等动态环境中的局限性，并探讨了替代 Kalman 滤波器的各种基于学习的运动模型。我们提出了一种名为 MambaTrack 的在线基于运动的跟踪器，它在挑战性的 DanceTrack 和 SportsMOT 数据集上优于所有现有的基于运动的跟踪器。此外，我们进一步利用状态空间模型在轨迹特征提取中的潜力，提出了一种名为 MambaTrack + 的跟踪器，在 DanceTrack 数据集上达到 56.1 HOTA 和 54.9 IDF1 的最新性能。