Soccer Panoptic Segmentation Dataset

Soccer Panoptic Segmentation Dataset

Datasets

Soccer Panoptic Segmentation Dataset

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Soccer Panoptic Segmentation Dataset

Use Case

Soccer Panoptic Segmentation Dataset

Description

Explore the Soccer Panoptic Segmentation Dataset, featuring 844 annotated frames and 9455 labels for player tracking, ball detection, and field segmentation.

Description:

The Soccer Panoptic Segmentation Dataset is a comprehensive collection of soccer match footage designed for advanced analysis and segmentation tasks. It consists of three high-definition video sequences obtained from YouTube, with a total of 844 frames and 9455 detailed annotations. This dataset enables fine-grained segmentation across seven categories: players, balls, goal lines, fields, backgrounds, referees, and pitch lines, providing a solid foundation for a wide range of soccer-related research.

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Key Dataset Information

  • Data Format: Video sequences
  • Video Length: 3 sequences (30 frames per second)
  • Total Frames: 844 annotated frames
  • Annotations: 9455 labeled instances across seven key classes
  • License: YouTube videos under the MIT License
  • Annotation Tool: Supervisely (Panoptic Segmentation)

Use Cases and Applications

  • Player Tracking: Track player movements throughout the game, useful for performance analysis, team strategies, and individual tracking.
  • Ball Tracking: Identify and predict ball trajectories for tactical insights.
  • Field Segmentation: Separate field areas for game analysis or strategy optimization.
  • Player Action Recognition: Detect and classify player actions during critical game moments.
  • Sports Analytics: Utilize advanced segmentation for insights into match dynamics and team performance.
  • Tactical Analysis: Analyze in-game strategies through detailed player and object tracking.
  • AI-Assisted Refereeing: Enhance decision-making processes by assisting referees with automated object detection.

Technical Details

  • Video Source: The dataset includes videos collected from YouTube, ensuring high accessibility and open-source usage under the MIT License.
  • Annotation Platform: Annotations were created using Supervisely, a robust tool for panoptic segmentation, ensuring precision and high-quality labeling.
  • Volume: The dataset comprises a total of 9455 labeled objects across 844 frames, providing a significant dataset for thorough research.

Advanced Use Cases

  • Player Movement Heatmaps: Use the dataset to generate heatmaps representing player movements across different areas of the field.
  • Real-Time Event Detection: Implement models for real-time detection of events such as goals, fouls, and offsides using the fine-grained annotations provided.
  • Action-Based Analysis: Evaluate specific player actions, such as passing, shooting, and defensive maneuvers, to analyze individual and team performance.
  • Trajectory Prediction: Build models to predict the trajectory of the ball and players, aiding in strategic decisions and match preparation.
  • Sports Broadcasting Enhancement: Enhance sports broadcasts with AI-driven insights and real-time segmentation of players and objects.

Conclusion

This dataset is a vital resource for those interested in sports analytics, soccer analysis, and AI-driven segmentation. Its comprehensive labels and high-quality video content make it ideal for developing cutting-edge machine learning models focused on tracking, recognition, and segmentation within sports environments. Whether for player tracking, tactical analysis, or AI-enhanced refereeing, this dataset offers unparalleled depth and detail for researchers.

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