SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

TL;DR

This paper provides a comprehensive analysis of 3D multi-object tracking methods, introduces a simple yet effective baseline called SimpleTrack, and critically examines current benchmarks to better reflect real-world challenges.

Contribution

It unifies existing 3D MOT methods into a framework, proposes SimpleTrack as a strong baseline, and analyzes benchmark limitations for future improvements.

Findings

SimpleTrack achieves state-of-the-art results on Waymo and nuScenes datasets.

Current benchmarks may not fully reflect real-world algorithm performance.

Analysis of failure cases guides future research directions.

Abstract

3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm. Despite their progress and usefulness, an in-depth analysis of their strengths and weaknesses is not yet available. In this paper, we summarize current 3D MOT methods into a unified framework by decomposing them into four constituent parts: pre-processing of detection, association, motion model, and life cycle management. We then ascribe the failure cases of existing algorithms to each component and investigate them in detail. Based on the analyses, we propose corresponding improvements which lead to a strong yet simple baseline: SimpleTrack. Comprehensive experimental results on Waymo Open Dataset and nuScenes demonstrate that our final method could achieve new state-of-the-art results with minor modifications. Furthermore, we take additional steps and rethink whether current benchmarks authentically reflect the ability of algorithms for real-world challenges. We delve into the details of existing benchmarks and find some intriguing facts. Finally, we analyze the distribution and causes of remaining failures in \name\ and propose future directions for 3D MOT. Our code is available at https://github.com/TuSimple/SimpleTrack.