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

Xinshuo Weng, Jianren Wang, David Held, Kris Kitani

Robotics Institute, Carnegie Mellon University

International Conference on Intelligent Robots and Systems (IROS), 2020

Oral Presentation


One-Sentence Summary

We proposed a 3D multi-object tracker based on a 3D Kalman filter that achieves state-of-the-art performance with the fastest speed (207.4 FPS).


Demo Video (15 minute oral presentation at IROS 2020)


Demo Video (3 minute short presentation at ECCVW 2020)


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.


Approach




BibTex

@article{Weng2020_AB3DMOT, 
author = {Weng, Xinshuo and Wang, Jianren and Held, David and Kitani, Kris}, 
journal = {IROS}, 
title = {{3D Multi-Object Tracking: A Baseline and New Evaluation Metrics}}, 
year = {2020} 
}
@article{Weng2020_AB3DMOT_eccvw, 
author = {Weng, Xinshuo and Wang, Jianren and Held, David and Kitani, Kris}, 
journal = {ECCVW}, 
title = {{AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics}}, 
year = {2020} 
}

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