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Online Hand Gesture Recognition

Overview

This project focuses on developing an online hand gesture recognition system. Utilizing advanced deep learning techniques, it aims to accurately recognize and interpret hand gestures in real-time. The system employs a sophisticated CNN-Transformer architecture to process and analyze video data, making it capable of understanding complex hand gestures.

Features

  • Real-Time Gesture Recognition: Detects and interprets hand gestures in real-time.
  • Advanced CNN-Transformer Model: Leverages the strengths of both CNNs for spatial feature extraction and Transformers for capturing temporal dynamics.
  • Variable-Length Sequence Handling: Accommodates gestures of varying durations with effective padding and masking strategies.
  • High Accuracy and Efficiency: Optimized for both high accuracy in gesture recognition and operational efficiency in online settings.

Requirements

  • Python 3.8 or above
  • PyTorch
  • Torchvision
  • CUDA (for GPU acceleration)
  • Other dependencies listed in requirements.txt

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/KenanKhauto/online-gesture-recognition-project.git
cd online-gesture-recognition-project
pip install -r requirements.txt