# luanshiyinyang/awesome-multiple-object-tracking

Resources for Multiple Object Tracking (MOT)

Repository: https://github.com/luanshiyinyang/awesome-multiple-object-tracking
Canonical: https://ross.abutalabs.com/products/awesome-multiple-object-tracking
License Family: other
Last push: 2025-10-07T06:54:10+00:00

## Health v2 (maintenance only)
Score: 52/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 45, release rhythm 35, longevity 100
- inputs: {"age_days": 2265, "days_push": 330, "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 1483, forks 186 (observed 2026-08-28T04:04:51.250178+00:00)

## What it is
A curated awesome-list of resources for Multiple Object Tracking (MOT), including review and algorithm papers, datasets, metrics, benchmark results, toolboxes, and courses. It focuses on online tracking methods and is actively maintained with papers through 2025.

## Use cases
- find papers on multiple object tracking
- learn about MOT algorithms and state of the art
- find datasets for training multi-object tracking models
- compare MOT benchmark results on MOT16/MOT17/MOT20
- find toolboxes for object tracking research
- get started with multi-object tracking research

## When to choose
- you are researching or surveying multi-object tracking methods
- you need a starting point of papers, datasets, and benchmarks for MOT
- you want to track the latest MOT publications by year

## When to avoid
- you need a ready-to-run tracking implementation rather than a resource list
- you need single-object tracking or offline/batch tracking methods
- you need production computer vision software

## Facets
- artifact type: learning-resource
- maturity: active
- function: computer-vision, machine-learning
- domain: computer-vision, machine-learning, awesome-lists
- platform: cross-platform
- tags: awesome-list, multiple-object-tracking, mot, curated-resources, papers, datasets, benchmarks

## Member repositories
- luanshiyinyang/awesome-multiple-object-tracking (main) score 52

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.250178+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 2026-08-30T04:34:03.307956+00:00, confidence not recorded.
  - readme: https://github.com/luanshiyinyang/awesome-multiple-object-tracking (fetched 2026-08-28T04:04:51.250178+00:00, sha a4cf45e6669f)
- Data as of 2026-08-30T08:39:29.467469+00:00.