AB3DMOT：3D多物体追跡のベースラインと新評価指標
AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics
LiDAR点群から得た3D検出に対し3Dカルマンフィルタとハンガリアン法を組み合わせたシンプルなリアルタイム3D多物体追跡システムを提案し、KITTIで207.4FPSを達成。さらに3D MOT評価ツールと3つの新指標を提案した。
著者: Xinshuo Weng, Jianren Wang, David Held, Kris Kitani
分類: cs.CV, cs.MA, cs.RO
原文アブストラクト
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.
