Abstract
End-to-end multi-object tracking (MOT) methods have recently achieved remarkable progress by unifying detection and association within a single framework. Despite their strong detection performance, these methods suffer from relatively low association accuracy. Through detailed analysis, we observe that object embeddings produced by the shared DETR architecture display excessively high inter-object similarity, as it emphasizes only category-level discrimination within single frames. In contrast, tracking requires instance-level distinction across frames with spatial and temporal continuity, for which current end-to-end approaches insufficiently optimize object embeddings. To address this, we introduce FDTA (From Detection to Association), an explicit feature refinement framework that enhances object discriminativeness across three complementary perspectives. Specifically, we introduce a Spatial Adapter (SA) to integrate depth-aware cues for spatial continuity, a Temporal Adapter (TA) to aggregate historical information for temporal dependencies, and an Identity Adapter (IA) to leverage quality-aware contrastive learning for instance-level separability. Extensive experiments demonstrate that FDTA achieves state-of-the-art performance on multiple challenging MOT benchmarks, including DanceTrack, SportsMOT, and BFT, highlighting the effectiveness of our proposed discriminative embedding enhancement strategy. The code is available at this https URL.
Abstract (translated)
最近，端到端的多目标跟踪（MOT）方法通过在单一框架内统一检测和关联取得了显著进展。尽管这些方法具有强大的检测性能，但在关联准确性方面仍然存在相对较低的表现。经过详细分析，我们发现由共享DETR架构生成的对象嵌入表现出过高的对象间相似性，因为这种架构仅强调单帧内的类别级别区分。相比之下，跟踪要求跨帧在空间和时间连续性上进行实例级别的区别，而当前的端到端方法对优化对象嵌入不足。 为了解决这个问题，我们提出了FDTA（从检测到关联），这是一种显式的特征增强框架，它通过三个互补的角度增强了对象的区分度。具体来说，我们引入了空间适配器（SA）来整合深度感知线索以实现空间连续性、时间适配器（TA）来聚合历史信息以捕捉时间依赖关系以及身份适配器（IA），用于利用质量感知对比学习增强实例级别的可分离性。 广泛的实验表明，FDTA在多个具有挑战性的MOT基准测试中取得了最先进的性能，包括DanceTrack、SportsMOT和BFT。这突显了我们提出的区分式嵌入增强策略的有效性。代码可在[此处](https://this-url.com)获取。
URL
https://arxiv.org/abs/2512.02392
