VisionPlay: Next-Gen Sports Analytics using Deep Learning

VisionPlay is an advanced sports analytics system leveraging deep learning and computer vision to analyze soccer match footage in real-time. By tracking players, referees, and the ball, VisionPlay provides actionable insights into player performance, team strategies, and game dynamics.

Demo

Project Overview

VisionPlay utilizes state-of-the-art algorithms to analyze soccer videos, offering comprehensive features for coaches, analysts, and fans:

• Multi-Object Detection and Tracking
• Player Identification and Team Assignment
• Ball Possession Analysis
• Performance Metrics Calculation
• Camera Movement Compensation
• Perspective Transformation
• Visual Annotations

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Key Components and Algorithms

1. Multi-Object Detection and Tracking

• Object Detection: Uses a fine-tuned YOLO (You Only Look Once) model to detect players, goalkeepers, referees, and the ball in real-time.
• Object Tracking: Employs ByteTrack for robust tracking across frames, ensuring accurate and stable tracking.

2. Player Identification and Team Assignment

• Team Assignment: Uses K-means clustering to assign players to teams based on jersey color.
• Player Tracking: Uniquely identifies and tracks individual players throughout the match.

3. Ball Possession Analysis

• Ball-Player Association: Identifies the player in possession of the ball for each frame.
• Team Possession Statistics: Calculates and displays real-time team ball control.

4. Performance Metrics Calculation

• Speed Calculation: Computes player speed based on movement between frames.
• Distance Tracking: Tracks total distance covered by each player.

5. Camera Movement Compensation

• Optical Flow: Estimates camera movement and applies compensation for accurate player positions.

6. Perspective Transformation

• Coordinate Mapping: Maps pixel coordinates to real field positions for spatial analysis.

7. Visual Annotations

• Bounding Boxes: Highlights players, referees, and the ball.
• Performance Metrics Display: Shows speed, distance, and team possession in real-time.

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#Training The YOLO model used for object detection was fine-tuned on a custom dataset containing labeled players, goalkeepers, referees, and the ball. Details of the training process can be found in the notebooks/Training_YOLO.ipynb notebook.

Installation and Setup

1. Clone the Repository

git clone https://github.com/your-username/VisionPlay.git
cd VisionPlay

Install Dependencies

Install the necessary Python libraries by running:

pip install -r requirements.txt

GPU Support for PyTorch

If you intend to leverage GPU acceleration, follow these steps to set up PyTorch with CUDA support:

CUDA Toolkit: Install CUDA Toolkit compatible with your GPU and your PyTorch version (CUDA 11.8 or above is recommended). NVIDIA Drivers: Ensure you have the latest NVIDIA drivers installed.

Running VisionPlay

To start analyzing a video with VisionPlay:

Place your video files in the videos/ folder. Run the main script to analyze the footage.