Executive Summary

As municipalities increasingly invest in sports infrastructure to promote health and activity, there is a growing need for advanced monitoring systems that can provide actionable insights. This business case outlines the development of an AI-powered computer vision system designed to monitor sports facilities, optimize resource management, and enhance community engagement. By leveraging data analytics, this system will empower local governments to make informed decisions about facility usage, maintenance, and future investments.

Problem Statement

Local governments face challenges in effectively managing sports facilities, including:

  • Difficulty in monitoring usage patterns and the condition of playing fields.
  • Inability to collect accurate data on visitor demographics and activities.
  • Challenges in ensuring the safety and upkeep of facilities, leading to potential deterioration.

Proposed Solution

Develop a comprehensive computer vision system that provides real-time monitoring and analytics for sports facilities, focusing on:

  • Counting and analyzing the number of visitors and their activities.
  • Assessing the condition of the lawns and predicting maintenance needs.
  • Generating reports that support data-driven decisions for future infrastructure investments.

Objectives

  • Optimized Resource Management: Enable municipalities to track usage and condition of sports fields for timely maintenance and investment.
  • Data-Driven Decision Making: Provide local authorities with reliable analytics to guide facility development and funding allocation.
  • Enhanced Community Engagement: Foster a better understanding of how community members utilize sports facilities, promoting active lifestyles.

Key Features

  1. Real-Time Monitoring: Deploy high-resolution cameras to capture images and analyze activity on sports fields.
  2. Activity Recognition: Use AI algorithms to identify different sports being played and gather demographic data on participants.
  3. Predictive Maintenance: Analyze wear patterns on the grass to predict when reseeding is needed.
  4. Heat Map Visualization: Generate heat maps to visualize usage intensity across different areas of the field.

Technologies Utilized

  • Computer Vision Algorithms: Implement object detection frameworks, such as YOLOv5 and RetinaNet, for accurate identification and tracking.
  • Python and C++: Develop backend software and algorithms, leveraging OpenCV for image processing.
  • MongoDB: Utilize a NoSQL database for storing analytics and recognition results.
  • Vue.js: Create a user-friendly web interface for local authorities to access real-time data and reports.

Economic Impact

The implementation of this computer vision system is expected to yield significant economic benefits:

  • Cost Savings: Reduced maintenance costs through timely intervention based on predictive analytics.
  • Informed Investments: Enhanced decision-making regarding the construction of additional facilities, driven by accurate usage data.
  • Community Health Benefits: Increased participation in sports activities leading to improved public health outcomes.

Conclusion

The proposed AI-driven computer vision system for monitoring sports facilities offers a transformative approach to community sports management. By providing real-time analytics and insights, local governments can make data-informed decisions that enhance facility usage, optimize maintenance, and promote active lifestyles. This business case advocates for investment in the development of this innovative solution, with the potential for widespread adoption in municipalities across Australia and beyond.