# gaomingqi/Track-Anything

Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.

Repository: https://github.com/gaomingqi/Track-Anything
Canonical: https://ross.abutalabs.com/products/track-anything
Language: Python
License: MIT
License Family: permissive
Topics: segment-anything, video-object-segmentation, interactive-tracking, track-anything, video-object-tracking, inpaint-anything
Last push: 2025-12-13T11:02:33+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 57, release rhythm 35, longevity 88
- inputs: {"age_days": 1240, "days_push": 263, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6994, forks 510 (observed 2026-08-28T04:09:52.408625+00:00)

## What it is
Track-Anything is an interactive tool for video object tracking and segmentation built on Segment Anything, XMem, and E2FGVI. Users specify objects to track via clicks, can correct or change tracked objects during tracking, and use results for downstream tasks like video inpainting and editing.

## Use cases
- track and segment objects in videos with click-based interaction
- annotate video object segmentation datasets visually
- segment objects across shot changes in video
- inpaint or remove objects from videos
- correct tracking regions when segmentation drifts
- prepare object masks for video editing workflows

## When to choose
- you need interactive, click-based video object segmentation rather than fully automatic pipelines
- you want a visual annotation tool for video segmentation datasets
- you need object masks as input for video inpainting or editing
- your videos contain shot changes requiring re-specification of tracked objects

## When to avoid
- you need fully automatic, hands-off video segmentation at scale
- you require real-time tracking performance in production
- you lack a GPU, since the models have significant GPU memory requirements
- you need lightweight integration into an existing application rather than a standalone tool

## Facets
- artifact type: application
- maturity: maintenance
- function: computer-vision, image-processing, video-processing, machine-learning
- domain: computer-vision, image-processing, artificial-intelligence
- platform: python
- tags: segment-anything, video-object-segmentation, video-inpainting, interactive-annotation, gradio, xmem, e2fgvi, video, gpu, web-server, docker

## Member repositories
- gaomingqi/Track-Anything (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:52.408625+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 2026-08-29T17:41:06.643214+00:00, confidence not recorded.
  - readme: https://github.com/gaomingqi/Track-Anything (fetched 2026-08-28T04:09:52.408625+00:00, sha 372cd118ef8a)
- Data as of 2026-08-30T08:39:29.467469+00:00.