Multi-Object Tracking For Strategic Sports Analysis

Overview

This project implements advanced computer vision techniques to track and analyze player movements in sports videos. Developed by Caleb Musfeldt, Sreeram Kondapalli, and Will Janes as part of their Data Science Capstone (Fall 2024), it aims to automate sports film analysis to help teams make informed strategic decisions.

Features

Player Tracking

• Real-time player detection and tracking
• Support for multiple sports (Basketball, Soccer, Football)
• Continuous tracking with position history
• Color-based detection for uniforms and helmets
• Multi-player tracking capabilities

Analysis Tools

• Heat map generation for player movement patterns
• Comprehensive descriptive statistics
• Player movement path visualization
• Speed and distance calculations
• Position tracking and trail visualization

Data Processing

• Integration with COCO dataset (330K images, >200K labeled)
• Processing of various video formats and resolutions
• Support for multiple simultaneous game analysis
• Pixel normalization for accurate measurements

Technical Implementation

Detection Methods

• HSV color space detection
• Morphological operations for noise reduction
• Contour analysis with area and shape filtering
• Circularity metrics for helmet detection
• Advanced tracking algorithms for player persistence

Visualization

• Movement path tracking
• Heat map generation
• Statistical overlays
• Player identification markers
• Real-time trail visualization

Challenges and Solutions

Current Challenges

1. Pixel Normalization

   • Dealing with varying video resolutions
   • Converting pixel measurements to real-world units

2. Player Tracking

   • Maintaining consistent tracking across frames
   • Handling player occlusions
   • Managing multiple simultaneous tracks

3. Field Detection

   • Handling inconsistent field colors
   • Dealing with varying lighting conditions
   • Processing multiple game views

Ethical Considerations

Privacy Concerns

• Player data privacy protection
• Compliance with legal requirements
• Ethical use of tracking data

Accessibility

• Addressing potential competitive advantages
• Ensuring fair access to technology
• Managing data usage rights

Future Development

Planned Features

1. Accurate Player Identification

   • Improved number recognition
   • Player-specific tracking
   • Enhanced identification persistence

2. Player Path Prediction

   • Movement pattern analysis
   • Predictive modeling
   • Strategic play anticipation

3. User Interface

   • Simple GUI for consumer use
   • Intuitive controls
   • Real-time analysis tools

Technical Roadmap

• Implementation of deep learning models
• Enhanced tracking algorithms
• Improved data visualization tools
• Real-time processing optimization

Acknowledgments

• Data source: Pexels.com for open source video material
• COCO dataset for training data