Sports

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sports is an Open Source tool that provides cool experiments at the intersection of Computer Vision and Sports. It offers solutions for football player tracking, 3D pose estimation, and team assignment using AI.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is sports?

sports is an open-source project by SkalskiP dedicated to exploring the intersection of Computer Vision and Sports. It features various experiments, including football player tracking using YOLOv5 and ByteTrack, 3D football player pose estimation with YOLOv7, and assigning players to teams based on uniform color using GPT-4V. The project is designed for researchers and developers interested in applying advanced AI techniques to sports analytics, offering practical examples and code for implementing these vision-based solutions. It serves as a valuable resource for understanding and replicating complex computer vision tasks in a sports context.

Best used for

Ideal for computer vision engineers and data scientists who need to analyze sports performance, develop automated tracking systems, and explore AI applications in sports. Especially valuable for researchers and developers working with football analytics and pose estimation.

Common actions

track sports players

estimate player pose

analyze sports video

apply computer vision

deepfakecollaborationautomated workflowlow-code/no-codeopen-sourceworkflowsgithub copilotface swapping"AI Agents"

Capabilities

Key features

Football player tracking
3D pose estimation
Team uniform color analysis
GPT-4V integration
YOLOv5/YOLOv7 implementation

Target Audience

data scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What specific computer vision models are used in the sports project?

The sports project utilizes YOLOv5 and ByteTrack for football player tracking, YOLOv7 for 3D football player pose estimation, and GPT-4V for assigning players to teams based on uniform color. These models are central to the various experiments conducted within the project.

Can this project be used for sports other than football?

While the current examples and experiments primarily focus on football, the underlying computer vision techniques like object detection (YOLOv5/YOLOv7) and tracking (ByteTrack) are generalizable. With appropriate training data, these methods could be adapted for other sports.

What kind of data is required to replicate the experiments in this project?

To replicate the experiments, you would typically need video footage of sports matches. For tasks like pose estimation, multiple camera angles might be beneficial, as demonstrated in the 3D pose estimation example. The project also leverages pre-trained models.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing

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