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Football Pitch Instance Segmentation Computer Vision Dataset

byMy ProjectsTask:
Instance Segmentation
License:CC BY 4.01.38k views62 downloads
See all 360 imagesFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing crossing_player_zone, pitchFootball Pitch Instance Segmentation Football-Pitches sample showing cross_recipient_zone, crossing_player_zone, pitch

About Football Pitch Instance Segmentation Dataset

Here are a few use cases for this project:

  1. Player Performance Analysis: Using the "Football Pitch Instance Segmentation" model, coaches and analysts can evaluate player movements and positions by segmenting the different regions of the pitch. This can help identify strengths, weaknesses, and areas of improvement for individual players or the whole team in different zones such as crossing, receiving, and general play.

  2. Tactical Analysis and Strategy Development: The segmentation of football pitches into crossing_player_zone, cross_recipient_zone, and pitch regions can help coaches and analysts design tailored tactics, set plays, and attacking/defensive strategies. By understanding the spatial distribution of these zones during matches, teams can develop strategies to exploit opportunities or mitigate threats.

  3. Enhanced Broadcasting and Visualization: Media outlets and content creators can use the "Football Pitch Instance Segmentation" model to create engaging visualizations and graphical representations of matches for viewers. Highlighting critical areas during live broadcasts or post-match analysis can help communicate complex strategic concepts to audiences more effectively.

  4. Fan Engagement and Interactive Content: The model can be used to create interactive content for fans, such as virtual replays or predicting the outcome of plays based on player positioning in different zones. This could make for a more engaging fan experience, either as standalone content or integrated with existing apps and platforms.

  5. Training and Simulation: By segmenting football pitches and analyzing player movements in different zones, advanced training simulations can be developed for players and coaches. These simulations can be used to test various tactical scenarios, enhance decision-making skills, and improve reaction times in a realistic and controlled environment.

Use Free Cross_recipient_zone, Crossing_player_zone and Pitch Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "cross_recipient_zone, crossing_player_zone, pitch"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ football-pitch-instance-segmentation_dataset,
  title = { Football Pitch Instance Segmentation Dataset },
  type = { Open Source Dataset },
  author = { My Projects },
  howpublished = { \url{ https://universe.roboflow.com/my-projects-ons8w/football-pitch-instance-segmentation } },
  url = { https://universe.roboflow.com/my-projects-ons8w/football-pitch-instance-segmentation },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { jun },
  note = { visited on 2026-07-29 },
}

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