发表机构
Industrial Systems Institute, Athena Research Center; University of Ioannina（雅典娜研究中心工业系统研究所; 约阿尼纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CDKF-Track，一种聚类感知数据驱动卡尔曼滤波框架，通过图拉普拉斯融合、冗余减少、数据驱动滤波和小波细化，在V2V4Real数据集上实现最高27.99%的跟踪精度提升。
AI 中文摘要
多目标跟踪（MOT）对于边缘AI感知系统至关重要，其中准确的目标定位和可靠的识别能够保障安全决策。在复杂的现实场景中，单智能体MOT面临遮挡、传感器噪声和场景理解不完整等问题。虽然多智能体系统通过利用共享信息提高了鲁棒性，但引入了冗余测量，导致错误的数据关联，并且仍然难以捕捉非线性目标动态。为解决这些挑战，我们提出了CDKF-Track，一种用于协同3D MOT的聚类感知数据驱动卡尔曼滤波框架。所提方法首先通过基于图拉普拉斯的公式融合多车辆3D LiDAR检测结果。然后，一种聚类感知的冗余减少方案将空间相关的检测分组，并选择代表性观测以减少输入到跟踪器的重复数据。由此产生的检测结果由数据驱动的卡尔曼滤波器处理，该滤波器从数据中学习目标运动动态，减少对预定义线性运动假设的依赖。此外，基于小波的时序细化模块利用小波的多分辨率分解特性来衰减短期位置波动并改善轨迹连续性。据我们所知，CDKF-Track是首个在协同3D MOT中联合解决检测级融合冗余和可学习运动建模的框架。在真实世界V2V4Real数据集上的实验结果表明，与最先进的多智能体MOT方法相比，CDKF-Track在跟踪精度上实现了高达27.99%的提升。
英文摘要
Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT suffers from occlusions, sensor noise, and partial scene understanding in complex real-world scenarios. While multi-agent systems improve robustness by exploiting shared information, they introduce redundant measurements that lead to false data associations, and still struggle to capture nonlinear object dynamics. To address these challenges, we propose CDKFTrack, a Cluster-aware Data-Driven Kalman Filtering framework for Cooperative 3D MOT. The proposed method first fuses multivehicle 3D LiDAR detections through a Graph Laplacian-based formulation. Then, a cluster-aware redundancy reduction scheme groups spatially related detections and selects representative observations to reduce duplicate inputs to the tracker. The resulting detections are processed by a data-driven Kalman filter that learns object motion dynamics from data, reducing dependence on predefined linear motion assumptions. Furthermore, a wavelet-based temporal refinement module leverages the multiresolution decomposition property of wavelets to attenuate shortterm positional fluctuations and improve trajectory continuity. To the best of our knowledge, CDKF-Track is the first framework to jointly address detection-level fusion redundancy and learnable motion modeling in cooperative 3D MOT. Experimental results on the real-world V2V4Real dataset indicate that CDKF-Track achieves up to 27.99% improvements in tracking accuracy over state-of-the-art multi-agent MOT methods.
