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tennis_match_tracking

https://github.com/vaibhavpandey-1221/tennis_match_tracking

Science Score: 44.0%

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    Scientific vocabulary similarity
    Low similarity (9.6%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: VaibhavPandey-1221
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 30.8 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

Tennis Ball Detection using YOLOv5 and Streamlit

This project demonstrates real-time tennis ball and player detection from video input using a custom-trained YOLOv5 model. The application is built with Streamlit for an interactive user interface.

Live Application

Features

  • Upload video files for processing
  • Displays the progress of object detection
  • Shows output video with tennis ball and player detection after processing

Setup Instructions

1. Clone the Repository

bash git clone <repository-url> cd yolov5

2. Install Dependencies

Make sure you have Python installed. Then, install the required libraries:

bash pip install -r requirements.txt

3. Model Setup

Place the custom-trained YOLOv5 model file in the yolov5/runs/exp/weights/best.pt.

4. Run the Application Locally

Run the following command in the yolov5 directory:

bash streamlit run app.py

File Structure

yolov5/ ├── app.py # Streamlit application file ├── runs/ │ └── exp/ │ └── weights/ │ └── best.pt # Trained YOLOv5 model weights └── data/ # Contains video input files

Usage

  1. Launch the Streamlit application.
  2. Upload a video file in .mp4 format.
  3. Wait for the detection to process; the completion percentage will be displayed.
  4. After processing, view the output video with detections highlighted.

Example

Upload a sample tennis match video to detect player movements and tennis ball positions, using real-time updates for progress.

Dependencies

  • Streamlit
  • PyTorch
  • OpenCV
  • YOLOv5

Owner

  • Login: VaibhavPandey-1221
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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