Problem
Research questions and friction points this paper is trying to address.
Multi-Object Tracking
Occlusions
Sensor Noise
Redundant Measurements
Nonlinear Object Dynamics
CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking
🤖 AI Summary
为解决多目标跟踪中的遮挡、噪声及非线性动态问题，提出CDKF-Track方法，通过融合多车3D LiDAR检测并结合数据驱动的卡尔曼滤波器提高跟踪精度。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cluster-aware Redundancy Reduction
Data-driven Kalman Filtering
Wavelet-based Temporal Refinement
Cooperative 3D Multi-Object Tracking
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