论文标题
AB3DMOT：3D多对象跟踪和新评估指标的基线
AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics
论文作者
论文摘要
3D多对象跟踪（MOT）对于诸如自动驾驶之类的应用至关重要。最近的工作着重于开发准确的系统，从而减少了对计算成本和系统复杂性的关注。相比之下，这项工作提出了一个简单的实时3D MOT系统，其性能很强。我们的系统首先从激光雷达点云中获得3D检测。然后，将3D Kalman滤波器和匈牙利算法的直接组合用于状态估计和数据关联。此外，诸如KITTI之类的3D MOT数据集评估了2D空间中的MOT方法，而标准化的3D MOT评估工具则缺少3D MOT方法的公平比较。我们提出了一种新的3D MOT评估工具以及三个新的指标，以全面评估3D MOT方法。我们表明，我们提出的方法在KITTI上实现了强劲的3D MOT性能，并且在Kitti数据集上以207.4美元的价格运行，在现代3D MOT系统中达到了最快的速度。我们的代码可在http://www.xinshuoweng.com/projects/ab3dmot上公开获取。
3D multi-object tracking (MOT) is essential to applications such as autonomous driving. Recent work focuses on developing accurate systems giving less attention to computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system with strong performance. Our system first obtains 3D detections from a LiDAR point cloud. Then, a straightforward combination of a 3D Kalman filter and the Hungarian algorithm is used for state estimation and data association. Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods. We propose a new 3D MOT evaluation tool along with three new metrics to comprehensively evaluate 3D MOT methods. We show that, our proposed method achieves strong 3D MOT performance on KITTI and runs at a rate of $207.4$ FPS on the KITTI dataset, achieving the fastest speed among modern 3D MOT systems. Our code is publicly available at http://www.xinshuoweng.com/projects/AB3DMOT.
