GraphCanon updated Sep 24, 2026 · GitHub synced Sep 24, 2026
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Decision brief
AI-Basketball-Referee is a computer vision system that uses YOLO for basketball detection and pose estimation to improve referee accuracy in real-time by detecting travels and double dribbles with precision.
Good fit when
- When needing precise and automated travel and double dribble detections during live games to enhance fairness.
- For collecting comprehensive data on player movements and ball interactions to refine machine learning models and statistics.
Avoid when
- If the system needs to run without real-time feedback capabilities, as AI-Basketball-Referee heavily relies on providing immediate detection of violations during gameplay.
- In scenarios prioritizing low-cost solutions, given its dependency on a custom YOLO model and extensive labeled data set for accurate detections.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (892d since push)
- As of Sep 24, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 24, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install AI-Basketball-RefereeSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A computer vision-based system using YOLO for basketball detection and pose estimation to improve referee accuracy in basketball games.
Capability facts
- Languages
- python
Source: github.language · Sep 24, 2026
Categories
Tags
README
AI Basketball Referee v2.0 Demo Video Purpose Sports are evolving day by day and the technology that supports these sports are evolving at an exponential rate. Many sports have implemented computer vision in order to improve referee calls and the overall fairness of the game. Ten...
For agents
This page has a .md twin and JSON over the API.