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Real-Time Sports Player and Event Tracking System Using Broadcast Video Analysis
  1. case
  2. Real-Time Sports Player and Event Tracking System Using Broadcast Video Analysis

Real-Time Sports Player and Event Tracking System Using Broadcast Video Analysis

alltegrio.com
Sports

Identifying Challenges in Sports Performance Analysis Using Broadcast Feeds

The client faces difficulties in analyzing player movements and key game events directly from broadcast camera feeds, aiming to avoid extra hardware costs. They require a scalable, real-time solution that can accurately track all players, annotate critical events with precise field coordinates, and deliver actionable insights for coaches and scouts. Existing methods are limited by hardware dependencies and lack of detailed, real-time analytics, hindering timely strategic decisions.

About the Client

A mid- to large-sized sports analytics company aiming to enhance performance analysis through AI-powered video processing without additional hardware investments.

Goals for Developing an AI-Powered Sports Video Analytics Platform

  • Develop a real-time player and event tracking system utilizing broadcast camera feeds to eliminate the need for additional hardware.
  • Enable precise annotation of onfield events with exact spatial coordinates for in-depth performance analysis.
  • Implement predictive analytics to assess team formations, player positioning, and game strategies to inform coaching decisions.
  • Create a scalable, secure cloud-based platform capable of handling large volumes of video data and annotations.
  • Provide metrics-focused insights to support team performance improvement and recruitment decision-making, aiming for enhanced decision speed and accuracy.

Core Functional Capabilities for Next-Gen Sports Video Analytics

  • Real-time comprehensive player tracking system that updates positions every second on the field for all players.
  • Event detection and precise coordinate marking for significant in-game actions.
  • Custom data annotation tools for high-quality labeling of game footage to train and refine machine learning models.
  • Predictive analytics modules to generate insights into team formations, player positioning, and performance metrics.
  • Integration with AI and machine learning models for activity recognition, continuous training, and model improvement.
  • Seamless cloud deployment on scalable platforms to ensure secure, fast data processing and storage.

Technology Stack for Building the Sports Analytics Platform

Python and C++ for core development
TensorFlow and PyTorch for machine learning frameworks
OpenCV for computer vision functionalities
AWS cloud services for scalable, secure infrastructure
PostgreSQL for database management
Docker and Kubernetes for deployment and orchestration

Necessary External System Integrations

  • Broadcast media feeds for input data
  • Existing team management and analytics dashboards for data presentation
  • Model training pipelines for continuous learning and deployment

Essential Non-Functional System Criteria

  • High scalability to process multiple live feeds simultaneously
  • Real-time data processing with minimal latency (target: updates every second)
  • Robust security measures for data confidentiality and integrity
  • High system uptime and reliability to support critical decision-making
  • Ease of integration with existing client systems and workflows

Anticipated Business Benefits of the Advanced Sports Video Analytics System

The platform aims to significantly improve team strategy formulation by providing instant, detailed insights into player movements and game events. It is expected to enhance recruitment accuracy through precise performance metrics and analytics. Overall, this solution can lead to faster decision-making, better tactical adjustments, and a competitive edge in the sports industry, mirroring prior success metrics such as improved decision speed, detailed evaluations, and strategic advantages.

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