# NirAharon/BoT-SORT

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Repository: https://github.com/NirAharon/BoT-SORT
Canonical: https://ross.abutalabs.com/products/bot-sort
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: multi-object-tracking, pytorch, yolov7, yolox, multi-object-tracker, multi-pedestrian-tracking, tracking-by-detection
Last push: 2024-08-08T18:45:51+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": 1524, "days_push": 755, "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 1522, forks 494 (observed 2026-08-28T04:04:57.993474+00:00)

## What it is
BoT-SORT is a state-of-the-art multi-object tracker that combines motion and appearance information with camera motion compensation and an improved Kalman filter state vector. It supports YOLOX and YOLOv7 detectors, multi-class tracking, and an optional Re-identification variant, ranking first on MOT17 and MOT20 benchmarks.

## Use cases
- track pedestrians in video with unique IDs
- multi-object tracking on MOT17/MOT20 benchmarks
- track multiple object classes with YOLOX or YOLOv7 detections
- compensate camera motion when tracking moving scenes
- re-identify people across occlusions in surveillance footage
- compare tracker performance using MOTA, IDF1, and HOTA metrics

## When to choose
- you need top-accuracy pedestrian or multi-class tracking with a PyTorch pipeline
- your videos have camera motion requiring compensation
- you want appearance-based re-identification to keep IDs stable through occlusions
- you are benchmarking trackers on MOTChallenge datasets

## When to avoid
- you need production deployment code, which is not yet provided
- you track objects without a compatible YOLO detector or want a detector-agnostic tracker
- you need a lightweight real-time tracker on CPU-only hardware
- you require Windows support out of the box (tested on Ubuntu)

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning
- domain: computer-vision, machine-learning, deep-learning
- platform: python
- tags: multi-object-tracking, tracking-by-detection, pedestrian-tracking, kalman-filter, camera-motion-compensation, re-identification, yolox, yolov7, pytorch, motchallenge, linux, gpu

## Member repositories
- NirAharon/BoT-SORT (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:57.993474+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 2026-08-30T04:31:46.341545+00:00, confidence not recorded.
  - readme: https://github.com/NirAharon/BoT-SORT (fetched 2026-08-28T04:04:57.993474+00:00, sha 2fd4a4c0e6e0)
- Data as of 2026-08-30T08:39:29.467469+00:00.