tryolabs/norfair

Health v2 · maintenance only

Adoption not part of the score

2676 stars · 275 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Norfair is a lightweight, customizable Python library for real-time multi-object tracking that works with any detector outputting (x, y) coordinates. It supports moving cameras, re-identification with appearance embeddings, and n-dimensional tracking, and can be dropped into existing video pipelines or used to build inference loops from scratch.

Use cases

• track players in sports video with a moving camera
• add multi-object tracking to my YOLO detector
• track pose keypoints across video frames
• count and follow objects in real-time video streams
• re-identify people after occlusion using appearance embeddings
• track objects in 3D point detections
• build a video inference loop with detection and tracking

When to choose

• you need fast, real-time tracking on top of an existing detector
• you want a minimal, modular tracking library that fits into custom pipelines
• you need support for moving cameras or re-identification
• you track keypoints or arbitrary coordinate detections, not just bounding boxes

When to avoid

• you need a full out-of-the-box video analytics application with UI and dashboards
• you require heavy-duty trackers with built-in state-of-the-art transformer-based association
• your project is not in Python

Facets

library · maturity stable

computer-vision video-processing machine-learning computer-vision machine-learning python cross-platform multi-object-tracking kalman-filter object-detection pose-estimation re-identification deepsort real-time video

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