Basketball Action Recognition

A computer vision-based tool that automatically recognizes and classifies key basketball actions (such as shooting, passing, dribbling, and more) in video footage. This tool is intended for use by players, coaches, and sports analysts to gain insights into game dynamics and improve performance.

Table of Contents

• Overview
• Setting Up the Virtual Environment
• Project Objectives
• Technology Stack
• Installation
• Dataset and Preprocessing
• Model Development
• Usage
• Results
• Contributors

Overview

The Basketball Action Recognition tool leverages computer vision and deep learning techniques to identify and classify basketball actions in real-time from video footage. By identifying key actions such as shooting, passing, and dribbling, this tool aims to provide actionable insights and improve game analysis.

Setting Up the Virtual Environment

To isolate dependencies and ensure consistent development, set up a virtual environment as follows:

1. Create and Activate the Virtual Environment:

   • On Windows:

     python -m venv .venv
     .venv\Scripts\activate

   • On macOS/Linux:

     python -m venv .venv
     source .venv/bin/activate

2. Install Dependencies: Once the virtual environment is activated, install all required packages with:

   pip install -r requirements.txt
   

Project Objectives

• Real-Time Action Detection: Accurately detect and classify basketball actions in real-time or near-real-time.
• User-Friendly Interface: Provide an intuitive web-based interface for uploading videos and viewing action classifications.
• Scalability and Performance: Ensure the model performs efficiently on varying video qualities and scales for large datasets.

Technology Stack

• Programming Language: Python
• Libraries & Frameworks:
  • Computer Vision: OpenCV
  • Deep Learning: TensorFlow/Keras or PyTorch
  • Data Manipulation: NumPy
  • Web Framework: Flask or Django
• Model Architecture: Convolutional Neural Networks (CNN) for feature extraction, with Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM) networks for capturing temporal sequences.
• Dataset: Custom-labeled basketball clips or publicly available sports datasets, like Kinetics-700.

Installation

To set up the project locally, follow these steps:

• Clone the Repository:

git clone https://github.com/your-username/basketball-action-recognition.git
cd basketball-action-recognition
  

• Install Required Libraries: Ensure you have Python installed, then install dependencies:

pip install -r requirements.txt

• Set Up Environment Variables (if needed): Configure any API keys or other settings in a .env file.

Dataset and Preprocessing

The project uses a subset of the Kinetics-700 dataset or custom-labeled basketball footage. Key preprocessing steps include:

• Frame Extraction: Extract video frames using OpenCV.
• Resizing and Normalization: Resize frames to a standard size and normalize pixel values.
• Data Annotation: Label video clips for basketball actions like "shooting," "passing," "dribbling," etc., using tools such as LabelImg if custom annotations are needed.

Model Development

The action recognition model has two main components:

• Feature Extraction: A CNN model processes each frame to extract spatial features.
• Temporal Modeling: An RNN or LSTM layer captures temporal sequences of actions.

The model is trained on labeled data and optimized for accuracy in real-time action detection.

Usage

To run the tool locally:

• Start the Web Interface:

python app.py

• Upload and Analyze Videos:
  • Access the interface at http://localhost:5000 (or the appropriate port).
  • Upload a video to see real-time action classifications.

Results

The model was trained on [specific datasets], achieving an accuracy of XX% in detecting key basketball actions. Sample output classifications for actions like shooting, passing, and dribbling are shown below:

Contributors

• List your team members here, e.g., Hetu Patel, Kushal Bhattad, Chris Grover, Eric Mergelas.