Football Match Semantic Segmentation Dataset

About the Dataset

1. Source Video and Frame Extraction

• Video Source: Frames are sourced from a publicly available football match video.
• Frame Sampling: 100 frames were extracted at 10-frame intervals to ensure a variety of in-game scenarios. This sampling method captures diverse moments in the match, from action-packed plays to strategic pauses.

2. Semantic Segmentation Annotations

Each frame has been meticulously annotated with semantic segmentation to identify critical components of a football match. The labels include:

• Advertisement: Highlighting banners and digital displays around the field.
• Field: Segmentation of the football pitch.
• Football: Identification of the ball during gameplay.
• Goal Bar: Precise labeling of the goal structure.
• Goalkeepers (1 & 2): Differentiation between the two goalkeepers.
• Referee: Accurate tracking of the referee’s position.
• Spectators: Recognition of audience areas in the stadium.
• Team 1 and Team 2: Segmentation of players from both teams.
• Background: Non-relevant elements outside the field of play.

3. Supporting Files

• Labels and Colors.csv: A comprehensive file listing all labels along with their corresponding RGB color codes for easy use in machine learning models.
• Images and Masks:
  • Images Folder: Contains 100 original frames from the match.
  • Masks Folder: Includes corresponding mask images with semantic segmentation annotations.

Advantages of the Dataset

1. High-Quality Annotations:
   Each frame has been manually labeled by Datarfly, experts in data annotation services, ensuring precision and reliability.
2. Diverse Scenarios:
   The sampling method captures a wide variety of game moments, making it ideal for training robust models.
3. Supports Multiple Applications:
   • Player tracking and behavior analysis.
   • Advertisement visibility studies for sports sponsorships.
   • Ball tracking for tactical insights and strategy optimization.
   • Spectator engagement analysis in stadium environments.
4. Comprehensive Color-Coding:
   The inclusion of RGB color codes simplifies the integration of the dataset into segmentation tasks, enhancing usability in computer vision pipelines.
5. Research and Development Friendly:
   Openly available for non-commercial use, this dataset is perfect for researchers, students, and professionals working in machine learning, AI, and sports technology.

Applications of the Dataset

• Build machine learning models for sports analytics.
• Train AI algorithms for real-time player and object tracking.
• Conduct research on audience engagement and advertising impact.
• Create innovative solutions for automated sports commentary and analytics.

Why Choose This Dataset?

The Football Match Semantic Segmentation Dataset is an essential tool for anyone aiming to bridge the gap between sports and AI. Whether you’re working on a university project, developing AI solutions for sports broadcasting, or enhancing gameplay analysis tools, this dataset offers the perfect foundation for groundbreaking research and development.