融合检测与查询的轻量级多目标跟踪框架，兼顾效率与精度。
Tracking by Detection and Query: An Efficient End-to-End Framework for Multi-Object Tracking
- 用冻结检测器+轻量关联器，实现高效端到端跟踪。
- 在DanceTrack上比领先检测类方法高6.0 IDF1，在MOT20上超越所有查询类方法。
- 参数量减少80%，推理提速37.5%，适合资源受限场景。
多目标跟踪（MOT）主要分为基于检测的跟踪（TBD）和基于查询的跟踪（TBQ）两类范式。TBD虽模块化高效，但关联流程碎片化，在复杂场景下鲁棒性不足；而TBQ虽能端到端建模语义，却因检测与关联强耦合导致训练成本高、推理慢。本文提出TBDQ-Net框架，融合二者优势：采用冻结检测器与轻量关联器，保障内在效率。针对MOT特有挑战，设计双流更新的基信息交互（BII）模块缓解任务冲突与遮挡，内容-位置对齐（CPA）模块精炼内容与位置特征，提升关联解码表示质量。在DanceTrack、SportsMOT和MOT20上广泛评估显示，该框架在复杂场景中取得良好效率-精度权衡。具体地，其在DanceTrack上比领先TBD方法高6.0 IDF1，于拥挤的MOT20上表现最优；相比MOTRv2，参数量减少约80%，实际推理速度提升37.5%。结果验证了轻量设计的有效性，为重型架构提供高效替代方案。源代码已公开于https://github.com/FaithFlow/TBDQ-Net。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) is primarily dominated by two paradigms: tracking-by-detection (TBD) and tracking-by-query (TBQ). While TBD offers modular efficiency, its fragmented association pipeline often limits robustness in complex scenarios. Conversely, TBQ enhances semantic modeling end-to-end but suffers from high training costs and slow inference due to the tight coupling of detection and association. In this work, we propose the tracking-by-detection-and-query framework, TBDQ-Net, to advance the synergy between TBD and TBQ paradigms. By integrating a frozen detector with a lightweight associator, this architecture ensures intrinsic efficiency. Within this streamlined framework, we introduce tailored designs to address MOT-specific challenges. Concretely, we alleviate task conflicts and occlusions through the dual-stream update of the Basic Information Interaction (BII) module. The Content-Position Alignment (CPA) module further refines both content and positional components, providing well-aligned representations for association decoding. Extensive evaluations on DanceTrack, SportsMOT, and MOT20 benchmarks demonstrate that TBDQ-Net achieves a favorable efficiency-accuracy trade-off in challenging scenarios. Specifically, TBDQ-Net outperforms leading TBD methods by 6.0 IDF1 points on DanceTrack and achieves the best performance among TBQ methods in the crowded MOT20 benchmark. Relative to MOTRv2, TBDQ-Net reduces trainable parameters by approximately 80% while accelerating practical inference by 37.5%. These results highlight TBDQ-Net as an efficient alternative to heavy architectures, showcasing the efficacy of lightweight design. Source code is publicly available at https://github.com/FaithFlow/TBDQ-Net.
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