sports

roboflowMIT活跃维护

面向体育场景的计算机视觉工具库，提供足球等运动的检测、关键点识别与图像嵌入能力，可直接用于球员追踪和赛事数据分析。

computer vision and sports

soccer-analytics-pr-demos发布于 2026-07-06 17:22 UTC5,488star +5 / 7 天673fork同步于 2026-11-10 23:36 UTC

体育视觉目标检测关键点检测足球分析图像嵌入

对比 GitHub computer-vision deep-learning deep-neural-networks football football-data

怎么上手

由 README 提炼，执行前请确认

原文

pip install git+https://github.com/roboflow/sports.git

硬件要求按 README 说明

README 未说明

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

Roboflow 开源的体育计算机视觉工具集，用于检测、分割、关键点与基础模型实验

面向体育场景的计算机视觉工具库，提供足球等运动的检测、关键点识别与图像嵌入能力，可直接用于球员追踪和赛事数据分析。

核心能力

提供球员、球、球场关键点等体育场景数据集
覆盖目标检测、图像分割、关键点检测与基础模型
聚焦球追踪、球衣号码识别、球员追踪与重识别
包含相机标定以支持速度与跑动距离等统计
提供可复用的体育分析演示与工具代码

容易踩的坑

尚无 Python 包，只能从源码安装且需 Python>=3.8；数据集需从 Roboflow Universe 单独下载；README 未说明模型权重与运行环境要求

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

用户点评

登录用户的真实使用记录，可按版本和硬件筛选

共 0 条

还没有人点评。第一个说出真实体验的人最有价值。

README 原文

未翻译

在 GitHub 打开

notebooks | inference | autodistill | maestro

👋 hello

In sports, every centimeter and every second matter. That's why Roboflow decided to use sports as a testing ground to push our object detection, image segmentation, keypoint detection, and foundational models to their limits. This repository contains reusable tools that can be applied in sports and beyond.

🥵 challenges

Are you also a fan of computer vision and sports? We welcome contributions from anyone who shares our passion! Together, we can build powerful open-source tools for sports analytics. Here are the main challenges we're looking to tackle:

Ball tracking: Tracking the ball is extremely difficult due to its small size and rapid movements, especially in high-resolution videos.
Reading jersey numbers: Accurately reading player jersey numbers is often hampered by blurry videos, players turning away, or other objects obscuring the numbers.
Player tracking: Maintaining consistent player identification throughout a game is a challenge due to frequent occlusions caused by other players or objects on the field.
Player re-identification: Re-identifying players who have left and re-entered the frame is tricky, especially with moving cameras or when players are visually similar.
Camera calibration: Accurately calibrating camera views is crucial for extracting advanced statistics like player speed and distance traveled. This is a complex task due to the dynamic nature of sports and varying camera angles.

💻 install

We don't have a Python package yet. Install from source in a Python>=3.8 environment.

pip install git+https://github.com/roboflow/sports.git

⚽ datasets

use case	dataset
⚽ soccer player detection
⚽ soccer ball detection
⚽ soccer pitch keypoint detection
🏀 basketball court keypoint detection
🏀 basketball jersey numbers ocr

Visit Roboflow Universe and explore other sport-related datasets.

🔥 demos

https://github.com/roboflow/sports/assets/26109316/7ad414dd-cc4e-476d-9af3-02dfdf029205

🏆 contribution

We love your input! Let us know what else we should build!