ModTrack实现跨传感器多视角追踪，无需重训练即可适配新设备和场景。
ModTrack: Sensor-Agnostic Multi-View Tracking via Identity-Informed PHD Filtering with Covariance Propagation
- 仅在检测阶段学习，其余环节用解析方法融合与追踪。
- 在WildTrack上达95.5 IDF1和91.4 MOTA，领先传统模块化方法21点以上。
- 支持雷达、可见光等多模态，更换感知模块即可部署到新场景。
多视角多目标追踪（MV-MOT）旨在对多个传感器观测的目标进行定位并保持身份一致。由于视角变化和遮挡，跨视图和时间的身份一致性难以维持。现有端到端方法虽能高精度追踪，但缺乏不确定性建模且依赖特定训练配置，难以泛化到不同传感器布局或数据集。本文提出ModTrack，一种模块化系统：将学习限制在检测与特征提取阶段，其余环节采用闭式解析方法。各传感器输出校准后的位置-协方差对（z, R），通过交叉视图聚类与精度加权融合获得统一估计（ẑ, Ř），用于身份分配与时序追踪。结合隐马尔可夫运动模型的反馈耦合型身份感知高斯混合概率假设密度滤波器，在漏检与严重遮挡下仍能维持身份。ModTrack在WildTrack上取得95.5 IDF1与91.4 MOTA，超越所有既往模块化方法超21点，媲美顶尖端到端方法。同一追踪核心可不修改直接迁移至MultiviewX与RadarScenes，仅需替换感知模块即可扩展至新领域与传感器模态。
原文摘要 · Abstract (English)
Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time. Recent end-to-end approaches address this by jointly learning 2D Bird's Eye View (BEV) representations and identity associations, achieving high tracking accuracy. However, these methods offer no principled uncertainty accounting and remain tightly coupled to their training configuration, limiting generalization across sensor layouts, modalities, or datasets without retraining. We propose ModTrack, a modular MV-MOT system that matches end-to-end performance while providing cross-modal, sensor-agnostic generalization and traceable uncertainty. ModTrack confines learning methods to just the \textit{Detection and Feature Extraction} stage of the MV-MOT pipeline, performing all fusion, association, and tracking with closed-form analytical methods. Our design reduces each sensor's output to calibrated position-covariance pairs $(\mathbf{z}, R)$; cross-view clustering and precision-weighted fusion then yield unified estimates $(\hat{\mathbf{z}}, \hat{R})$ for identity assignment and temporal tracking. A feedback-coupled, identity-informed Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with HMM motion modes uses these fused estimates to maintain identities under missed detections and heavy occlusion. ModTrack achieves 95.5 IDF1 and 91.4 MOTA on \textit{WildTrack}, surpassing all prior modular methods by over 21 points and rivaling the state-of-the-art end-to-end methods while providing deployment flexibility they cannot. Specifically, the same tracker core transfers unchanged to \textit{MultiviewX} and \textit{RadarScenes}, with only perception-module replacement required to extend to new domains and sensor modalities.
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