https://github.com/salvino72/openvino-Tennis-Posture

OpenVINO: Deciphering Tennis Posture with Artificial Intelligence
https://github.com/salvino72/openvino-Tennis-Posture

Last synced: about 1 month ago
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OpenVINO: Deciphering Tennis Posture with Artificial Intelligence

• Host: GitHub
• URL: https://github.com/salvino72/openvino-Tennis-Posture
• Owner: salvino72
• License: gpl-3.0
• Created: 2023-12-06T09:59:11.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-06T10:16:56.000Z (about 1 year ago)
• Last Synced: 2024-05-22T03:12:08.352Z (7 months ago)
• Language: Python
• Size: 21.5 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-openvino - OpenVINO Tennis Posture - Deciphering Tennis Posture with Artificial Intelligence (Table of content / AI Computer Vision)

README

        
# OpenVino Tennis Posture

OpenVINO: Deciphering Tennis Posture with Artificial Intelligence

Discover how AI can revolutionize the world of tennis by analyzing players' posture in detail. Thanks to OpenVINO, the application provides valuable feedback on styles and techniques, taking training to the next level.

At this moment it is a basic version, just for the purpose of testing the efficiency of some technologies. For an educational purpose we only analyze some behaviors and shots made by the tennis player.

In tennis, every detail counts. As coaches seek every advantage possible, technology emerges as an essential ally in providing valuable insights. It's not just about more advanced rackets or lighter sneakers, but also how AI can help decipher the nuances of human movement. Our application, backed by the power of OpenVINO, aims to do just that: analyze a tennis player's posture, offering detailed information on their techniques and movements.

More information on the website https://fidacaro.com/openvino-tennis/ 



### How does it work?

The heart of this application lies in its ability to process and analyze images using a deep neural network. This is done through OpenVINO, a platform provided by Intel that accelerates deep neural network inference operations.

After uploading a player image, the application proceeds with a series of steps:

1- Preprocessing: Adapt the image to the dimensions required by the neural network.

2 - Inference: Analyze the image to identify key points of human posture.

3 - Post-processing: Translates the results of the inference into understandable information, such as the position of the various limbs and the relationship between them.

4 - Analysis: Based on the key points detected, the application can infer specific actions, such as whether a player is executing a serve.

## Possibility of using the video source to analyze some tennis player positions using OpenVino



Human movement analysis plays a fundamental role in improving athletic performance, especially in complex sports such as tennis. The ability to automatically recognize and evaluate specific poses during a game can offer valuable insights to players and their coaches.

In the context of tennis, one of the most technical and crucial movements, for example, is the serve. Using the human pose estimation model, human-pose-estimation-0005, you can analyze each frame of a video sequence and precisely identify the position of the player's joints.