Accurate data association is crucial in reducing confusion, such as ID switches and assignment errors, in multi-object tracking (MOT). However, existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues, leading to confusion among detections, trajectories, and associations when performing simple global data association. To address this issue, we propose a simple, versatile, and highly interpretable data association approach called Decomposed Data Association (DDA). DDA decomposes the traditional association problem into multiple sub-problems using a series of non-learning-based modules and selectively addresses the confusion in each sub-problem by incorporating targeted exploitation of new cues. Additionally, we introduce Occlusion-aware Non-Maximum Suppression (ONMS) to retain more occluded detections, thereby increasing opportunities for association with trajectories and indirectly reducing the confusion caused by missed detections. Finally, based on DDA and ONMS, we design a powerful multi-object tracker named DeconfuseTrack, specifically focused on resolving confusion in MOT. Extensive experiments conducted on the MOT17 and MOT20 datasets demonstrate that our proposed DDA and ONMS significantly enhance the performance of several popular trackers. Moreover, DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets, significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA, IDF1, AssA. This validates that our tracking design effectively reduces confusion caused by simple global association.

翻译：精确的数据关联对于减少多目标跟踪（MOT）中的混淆（如身份切换和分配错误）至关重要。然而，现有的先进方法常忽视轨迹间的多样性以及运动与外观线索中的歧义和冲突，导致在执行简单全局数据关联时，检测、轨迹和关联之间产生混淆。为解决这一问题，我们提出了一种简单、通用且具有高可解释性的数据关联方法——解耦数据关联（DDA）。DDA利用一系列非学习型模块将传统关联问题分解为多个子问题，并通过针对性引入新线索的利用来选择性处理每个子问题中的混淆。此外，我们引入了遮挡感知非极大值抑制（ONMS），以保留更多被遮挡的检测结果，从而增加与轨迹关联的机会，并间接减少因漏检导致的混淆。最终，基于DDA和ONMS，我们设计了一款名为DeconfuseTrack的强大多目标跟踪器，其专注于解决MOT中的混淆问题。在MOT17和MOT20数据集上的大量实验表明，所提出的DDA和ONMS显著提升了多种主流跟踪器的性能。此外，DeconfuseTrack在MOT17和MOT20测试集上取得了最先进的性能，在HOTA、IDF1、AssA等指标上显著优于基线跟踪器ByteTrack。这验证了我们的跟踪设计能有效减少简单全局关联带来的混淆。