发表机构
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出GrayTrack，利用道路约束粒子滤波融合稀疏直接观测与第三方弱间接观测，在CARLA仿真中显著降低轨迹误差和丢失率，扩展车辆跟踪能力。
AI 中文摘要
车辆跟踪对于从城市交通、公共安全到安保和国防等应用至关重要。传统跟踪依赖于直接访问能够提供车辆身份和位置等强观测的传感器。然而，在实践中，所有权、隐私、成本和操作限制等因素可能限制直接可访问的传感器，导致观测稀疏和跟踪间隙较长。同时，环境中可能存在许多额外的第三方传感资产，但在原始数据层面无法访问，阻碍了它们直接集成到跟踪系统中。在这项工作中，我们研究了具有不确定时空线索的弱间接观测是否能够补充稀疏的直接传感以用于车辆跟踪。具体来说，我们提出了GrayTrack，它使用道路约束粒子滤波器将弱匿名事件与稀疏直接观测融合。我们构建了一个CARLA-Mininet-WiFi流水线，在受控条件下评估系统，从可访问的摄像头生成直接观测，并从第三方摄像头生成间接观测。我们的基于学习的检测器在匿名车辆通行方面达到了0.989的F1分数。此外，纳入第三方间接观测将轨迹RMSE降低了60.1%，并将灾难性轨迹丢失从35.8%降至0.3%。这些结果表明，GrayTrack能够有效利用弱间接观测来扩展跟踪能力。
英文摘要
Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong observations such as vehicle identity and location. In practice, however, factors such as ownership, privacy, cost, and operational constraints may limit directly accessible sensors, leaving sparse observations and long tracking gaps. Meanwhile, many additional third-party sensing assets may be present across the environment but remain inaccessible at the raw-data level, preventing their direct integration into the tracking system. In this work, we investigate whether weak, indirect observations with uncertain spatial and temporal cues can complement sparse direct sensing for vehicle tracking. Specifically, we propose GrayTrack, which fuses weak anonymous events with sparse direct observations using a road-constrained particle filter. We build a CARLA-Mininet-WiFi pipeline to evaluate the system under controlled conditions, generating direct observations from accessible cameras and indirect observations from third-party cameras. Our learning-based detector achieves an F1 score of 0.989 for anonymous vehicle passages. Further, incorporating indirect third-party observations reduces trajectory RMSE by 60.1% and catastrophic track loss from 35.8% to 0.3%. These results demonstrate that GrayTrack can effectively exploit weak indirect observations to extend tracking capabilities.
Comments7 pages, accepted to the 6th International Workshop on the Internet of Things for Adversarial Environments (IoTAE), IEEE MILCOM 2026
