In the competitive world of professional soccer, teams are always on the lookout for innovative ways to gain an edge. One groundbreaking innovation is the use of advanced computer vision to analyze game footage. This case study delves into the development of a "Bird's Eye View" system for soccer game analysis by a team of computer vision researchers and sports analysts.
The Challenge:
How do you turn 2D video footage of soccer games into a near-3D experience that gives coaches a top-down view of the field? This new perspective helps assess player positioning, team formations, and overall game strategy.
The main hurdles included:
1. Accurately detecting and tracking players and the ball
2. Differentiating between teams based on jersey colors
3. Transforming the perspective to create a top-down view
4. Processing video in real-time or near-real-time
The Solution:
Our team crafted a multi-step pipeline to achieve the Bird's Eye View:
1. Object Detection: We used YOLOv5, a top-tier object detection algorithm, trained on a custom dataset to spot players and the ball.
2. Tracking: Implemented Deep SORT for real-time tracking of players across frames, ensuring each player has a unique ID.
3. Color Detection: K-means clustering was applied to identify jersey colors, which helped distinguish teams.
4. Perspective Transformation: A homography matrix transformed the detected coordinates from the camera view to a top-down perspective.
Implementation Details
Object Detection:
Custom-trained YOLOv5 on a soccer-specific dataset
- Classes: 'player' "referee" and 'ball'
Tracking
- Deep SORT algorithm for consistent player identification across frames
Color Detection
- K-means clustering on player bounding boxes
- Adjusted for the dominant green color of the field
- Mapped detected colors to a predefined palette for consistency
Perspective Transformation
- Homography matrix to shift coordinates
- Formula: [x', y', w'] = [x, y, 1] * M, with M as the perspective matrix
Accuracy Achieved 80% Right Now.
Tech Stack
Python: DeepSort, YOLOv5
System: i5 9th gen, 24 GB RAM, 6 GB Geforce 1660 Ti
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