ifzhang/FairMOT

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4244 stars · 924 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

FairMOT is a research implementation of a one-shot multi-object tracking model that jointly performs object detection and re-identification in a single network. It achieves state-of-the-art results on MOT challenge benchmarks at around 30 FPS.

Use cases

• track multiple objects in video in real time
• jointly detect and re-identify pedestrians in surveillance footage
• reproduce state-of-the-art results on MOT16/MOT17/MOT20 benchmarks
• run multi-object tracking at 30 FPS on GPU
• fine-tune a tracker on custom video datasets
• compare one-shot tracking baselines for research

When to choose

• you need a well-cited, proven multi-object tracking baseline with strong MOT benchmark accuracy
• you want joint detection and re-identification in one network for real-time tracking
• you are doing computer vision research on tracking and need a reproducible reference implementation

When to avoid

• you need a production-maintained tracker with active updates and easy pip installation
• you work outside Python/PyTorch or lack a GPU
• you need lightweight tracking on CPU or edge devices without tuning

Facets

library · maturity maintenance

computer-vision machine-learning deep-learning image-processing computer-vision artificial-intelligence deep-learning python multi-object-tracking object-detection re-identification one-shot-tracker real-time research-code pytorch linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ifzhang/FairMOT")