Project Summary

The client wanted a technologically advanced automated system efficient for reviewing the recorded matches thoroughly. The primary goal was to offer comprehensive and meaningful feedback on the player’s performance, including areas for improvement and performance highlights. Also, they needed a system to identify specific patterns, strengths, and weaknesses to help the coach develop effective training strategies and enhance the players’ overall performance.

Technical Stack

  • Mern Stack
  • MySQL
  • Python
  • Redis
  • MongoDB
  • Firebase
  • Industry

    Information Technology

  • region
  • Region

    USA

  • project-size
  • Project Size

    $8,000-$12,000

Highlights

Player Position Monitoring

Automated AI Analysis

Self-Learning AI Efficiency

Graphical Representation of Results

Challenges

  • Determine a key event or location in the badminton match for player tracking.
  • Calculate the timestamp to assess if a player is positioned within a predefined court region.
  • Identify the match's critical point to pinpoint the players' location.
  • Use timestamp calculations to confirm a player's presence in a specific region.

Technical Challenges

  • Due to the camera's wall view, we cannot utilize the detected player's center point, necessitating the computation of the leg point.
  • Developing an ETL pipeline to ingest real-time data, enabling instant predictions for subsequent analysis.
  • Streamlining the inference time to process and produce CSV files containing the necessary information efficiently.

Solutions

  • A dynamic desktop application, enhanced by AI models like computer vision models for player and shuttlecock detection, extracts essential play-related data, monitoring players' positions and movements. It continuously assesses the leg point's location and its transitions between regions. The coach receives a graphical representation of the resulting CSV, facilitating a comprehensive match analysis in a matter of minutes, with the ability to correct player errors.
  • The AI models were designed here to maintain ongoing self-learning capabilities, ensuring adaptability to real-time scenarios even after deployment. These functionalities and AI models are seamlessly integrated into the desktop application, delivering high efficiency.

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Benefits Post-Implementation

With the usage of AI models & user-friendly desktop applications, clients benefited in the following ways:

  • This automated AI system can analyze an hour's video in just 2 minutes, providing valuable insights through graphical representations.
  • The precise end-to-end system has resulted in time savings for coaches.
  • no.-of-resources
  • No. of Developers

    04

  • time-frame
  • Time Frame

    July 2022- March 2023

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