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

TL;DR

This paper introduces a simple, real-time 3D multi-object tracking system using classical methods, along with new evaluation metrics and tools, achieving state-of-the-art performance and high speed on benchmarks.

Contribution

The work presents a lightweight 3D MOT system with new evaluation metrics and tools, enabling fair comparison and demonstrating competitive results without relying on 2D data.

Findings

Achieves state-of-the-art performance on KITTI and nuScenes benchmarks.

Runs at 207.4 FPS, the fastest among modern MOT systems.

Provides new evaluation metrics and an open-source toolkit for 3D MOT assessment.

Abstract

3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to practical considerations such as computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system. 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 the 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods. Therefore, we propose a new 3D MOT evaluation tool along with three new metrics to comprehensively evaluate 3D MOT methods. We show that, although our system employs a combination of classical MOT modules, we achieve state-of-the-art 3D MOT performance on two 3D MOT benchmarks (KITTI and nuScenes). Surprisingly, although our system does not use any 2D data as inputs, we achieve competitive performance on the KITTI 2D MOT leaderboard. Our proposed system runs at a rate of $207.4$ FPS on the KITTI dataset, achieving the fastest speed among all modern MOT systems. To encourage standardized 3D MOT evaluation, our system and evaluation code are made publicly available at https://github.com/xinshuoweng/AB3DMOT.