AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics

TL;DR

This paper introduces a simple, real-time 3D multi-object tracking system using LiDAR data, along with new evaluation metrics and tools, achieving high accuracy and speed on the KITTI dataset.

Contribution

Proposes a lightweight 3D MOT system with strong performance and introduces new evaluation metrics and tools for fair comparison.

Findings

Achieves 207.4 FPS on KITTI dataset

Outperforms existing methods in speed and accuracy

Provides publicly available code for reproducibility

Abstract

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.