提出自适应融合框架，让传统与现代追踪预测互补，提升多目标追踪准确率。
PlugTrack: Multi-Perceptive Motion Analysis for Adaptive Fusion in Multi-Object Tracking
- 通过多感知运动分析动态调整卡尔曼滤波与数据驱动预测的权重
- 在MOT17/MOT20上性能显著提升，DanceTrack达顶尖水平
- 适合需要高精度、强泛化的多目标追踪场景
多目标追踪（MOT）普遍采用检测后追踪范式，卡尔曼滤波因计算高效成为标准运动预测器，但难以处理非线性运动模式。而近年的数据驱动预测器虽能捕捉复杂非线性动态，却存在领域泛化能力弱和计算开销高的问题。通过广泛分析发现，在以非线性运动为主的多个数据集上，卡尔曼滤波仍能在高达34%的情况下表现优于数据驱动预测器，表明真实追踪场景中同时包含线性与非线性运动特征。为此，我们提出PlugTrack，一种通过多感知运动理解实现自适应融合的新型框架。该方法利用多感知运动分析生成动态融合系数，实现卡尔曼滤波与数据驱动预测器的互补。PlugTrack在MOT17/MOT20上取得显著性能提升，并在DanceTrack上达到当前最优水平，且无需修改现有运动预测器。据我们所知，PlugTrack是首个通过自适应融合连接经典与现代运动预测范式的多目标追踪框架。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where Kalman filters serve as the standard motion predictor due to computational efficiency but inherently fail on non-linear motion patterns. Conversely, recent data-driven motion predictors capture complex non-linear dynamics but suffer from limited domain generalization and computational overhead. Through extensive analysis, we reveal that even in datasets dominated by non-linear motion, Kalman filter outperforms data-driven predictors in up to 34\% of cases, demonstrating that real-world tracking scenarios inherently involve both linear and non-linear patterns. To leverage this complementarity, we propose PlugTrack, a novel framework that adaptively fuses Kalman filter and data-driven motion predictors through multi-perceptive motion understanding. Our approach employs multi-perceptive motion analysis to generate adaptive blending factors. PlugTrack achieves significant performance gains on MOT17/MOT20 and state-of-the-art on DanceTrack without modifying existing motion predictors. To the best of our knowledge, PlugTrack is the first framework to bridge classical and modern motion prediction paradigms through adaptive fusion in MOT.
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