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

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arxiv 2008.08063 v1 pith:G6AENE4K submitted 2020-08-18 cs.CV cs.MAcs.RO

Xinshuo Weng , Jianren Wang , David Held , Kris Kitani This is my paper
classification cs.CVcs.MAcs.RO
keywords evaluationkittimethodssystemab3dmotevaluatemetricsmulti-object
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs

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    DENALI is the first large-scale real-world dataset of space-time histograms from low-cost LiDARs for training models to perceive hidden objects via multi-bounce light cues.

  2. PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment

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    A modular edge-based LiDAR framework that automatically curates site-specific training data, predicts trajectories, and flags intersection conflicts via TTC and predicted post-encroachment time.

  3. From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception

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    A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios...

  4. GateMOT: Q-Gated Attention for Dense Object Tracking

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    GateMOT proposes Q-Gated Attention to enable linear-complexity, spatially aware attention for state-of-the-art dense object tracking on benchmarks like BEE24.

  5. Radar-Informed 3D Multi-Object Tracking under Adverse Conditions

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    RadarMOT improves 3D multi-object tracking accuracy by using radar point clouds as direct observations to refine states and recover missed objects, achieving 12.7% higher AMOTA at long range and up to 10.3% in adverse...

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    A 0.5B LLM associates open-vocabulary 3D detections via trajectory sequence completion, raising novel-category AMOTA on nuScenes from 2.2% to 22.4%.

  7. GRASPTrack: Geometry-Reasoned Association via Segmentation and Projection for Multi-Object Tracking

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    A depth-aware MOT tracker using mask-guided 3D point clouds, voxelized 3D IoU association, adaptive Kalman noise, and 3D motion consistency surpasses prior TBD methods on MOT17, MOT20, and DanceTrack.

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  11. Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems

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