# FoundationVision/ByteTrack

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Repository: https://github.com/FoundationVision/ByteTrack
Canonical: https://ross.abutalabs.com/products/bytetrack
Language: Python
License: MIT
License Family: permissive
Topics: multi-object-tracking, real-time, deployment, pytorch
Last push: 2024-06-19T11:45:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1832, "days_push": 805, "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 6654, forks 1141 (observed 2026-08-28T04:09:47.374124+00:00)

## What it is
ByteTrack is a PyTorch-based multi-object tracking (MOT) library implementing the ECCV 2022 paper 'Multi-Object Tracking by Associating Every Detection Box'. It achieves state-of-the-art tracking accuracy (80.3 MOTA on MOT17) at real-time speeds by associating low-score detection boxes with tracklets to recover occluded objects.

## Use cases
- track multiple objects in video with bounding boxes and identities
- track pedestrians in surveillance footage in real time
- recover occluded objects that low-score detectors miss
- improve IDF1 scores of existing trackers with a better association method
- run multi-object tracking at 30 FPS on a single GPU
- benchmark a tracker on MOT17 and MOT20 datasets

## When to choose
- you need state-of-the-art multi-object tracking accuracy with real-time performance
- your detections include low-confidence boxes for occluded objects that other trackers discard
- you want a well-cited, research-backed tracker with pretrained models and deployment support
- you need a tracker that can be integrated on top of various state-of-the-art detectors

## When to avoid
- you need simple single-object tracking rather than multi-object tracking
- you cannot run a GPU or need CPU-only real-time tracking
- you need a maintained project with frequent updates, as development has slowed since 2022
- you need out-of-the-box tracking without training or configuring a detector

## Facets
- artifact type: library
- maturity: stable
- function: computer-vision, machine-learning, image-processing
- domain: computer-vision, artificial-intelligence, deep-learning
- platform: python, cross-platform
- tags: multi-object-tracking, object-detection, pytorch, real-time, eccv-2022, video-analytics, gpu, linux

## Member repositories
- FoundationVision/ByteTrack (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:47.374124+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 2026-08-29T17:43:07.022909+00:00, confidence not recorded.
  - readme: https://github.com/FoundationVision/ByteTrack (fetched 2026-08-28T04:09:47.374124+00:00, sha cadd80c640f1)
- Data as of 2026-08-30T08:39:29.467469+00:00.