# xinshuoweng/AB3DMOT

(IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"

Repository: https://github.com/xinshuoweng/AB3DMOT
Canonical: https://ross.abutalabs.com/products/ab3dmot
Homepage: http://www.xinshuoweng.com/
Language: Python
License: NOASSERTION
License Family: other
Topics: computer-vision, machine-learning, robotics, tracking, 3d-tracking, multi-object-tracking, real-time, evaluation-metrics, evaluation, 3d-multi-object-tracking, 2d-mot-evaluation, kitti, 3d-mot, 3d-multi, kitti-3d
Last push: 2024-04-03T19:30:20+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2632, "days_push": 882, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1845, forks 415 (observed 2026-08-28T04:05:43.391770+00:00)

## What it is
Official Python implementation of AB3DMOT, a simple real-time baseline for 3D multi-object tracking using oriented 3D bounding boxes from LiDAR point clouds, with new evaluation metrics (e.g., sAMOTA). Supports KITTI and nuScenes datasets.

## Use cases
- track 3d objects from lidar point clouds
- evaluate 3d multi-object tracking on kitti
- baseline 3d mot system for autonomous driving
- compute samota and mot evaluation metrics
- run multi-object tracking on nuscenes
- compare 3d tracking algorithms against a baseline

## When to choose
- you need a simple, real-time 3D MOT baseline for research comparison
- you want standardized 3D/2D MOT evaluation metrics on KITTI or nuScenes
- you work on autonomous driving perception with LiDAR detections

## When to avoid
- you need a production-ready tracking system with learned association models
- you track from camera images only without 3D detections
- you need active development or commercial support

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, benchmarking, simulation
- domain: computer-vision, autonomous-vehicles, robotics, machine-learning
- platform: python, cross-platform
- tags: 3d-multi-object-tracking, kalman-filter, lidar, kitti, nuscenes, evaluation-metrics, research-code, point-cloud, linux

## Member repositories
- xinshuoweng/AB3DMOT (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:43.391770+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:17:55.621512+00:00, confidence not recorded.
  - readme: https://github.com/xinshuoweng/AB3DMOT (fetched 2026-08-28T04:05:43.391770+00:00, sha 367262d7d300)
- Data as of 2026-08-30T08:39:29.467469+00:00.