用图信号处理优化多车协同3D目标追踪，提升定位精度
Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing
- 构建检测框构成的全连接图，用图拉普拉斯优化位置误差
- 在V2V4Real数据集上显著超越DMSTrack等基线方法
- 适合需要高精度多车协同感知的自动驾驶系统
多目标跟踪（MOT）在自动驾驶系统中至关重要，为高级感知与精确路径规划奠定基础。然而，单智能体MOT受限于遮挡、传感器故障等问题，难以全面感知环境。因此，融合多智能体信息对环境理解至关重要。本文提出一种新型协作式MOT框架，通过构建基于检测框的全连接图拓扑，建立并求解图拓扑感知优化问题，以融合多车信息。利用图拉普拉斯处理技术平滑检测框位置误差，挖掘多智能体检测间的内在一致性，并在两个阶段优化检测框关联与目标追踪，提升定位与追踪精度。在真实世界数据集V2V4Real上进行的广泛评估显示，所提方法在多个测试序列中显著优于基线框架，包括最先进的深度学习方法DMSTrack和V2V4Real。
原文摘要 · Abstract (English)
Multi-Object Tracking (MOT) plays a crucial role in autonomous driving systems, as it lays the foundations for advanced perception and precise path planning modules. Nonetheless, single agent based MOT lacks in sensing surroundings due to occlusions, sensors failures, etc. Hence, the integration of multiagent information is essential for comprehensive understanding of the environment. This paper proposes a novel Cooperative MOT framework for tracking objects in 3D LiDAR scene by formulating and solving a graph topology-aware optimization problem so as to fuse information coming from multiple vehicles. By exploiting a fully connected graph topology defined by the detected bounding boxes, we employ the Graph Laplacian processing optimization technique to smooth the position error of bounding boxes and effectively combine them. In that manner, we reveal and leverage inherent coherences of diverse multi-agent detections, and associate the refined bounding boxes to tracked objects at two stages, optimizing localization and tracking accuracies. An extensive evaluation study has been conducted, using the real-world V2V4Real dataset, where the proposed method significantly outperforms the baseline frameworks, including the state-of-the-art deep-learning DMSTrack and V2V4Real, in various testing sequences.
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