Publication Information
- Authors: Rishav Nath Pati, et al.
- Category: Research Publication
- Topic: Computer Vision, Machine Learning
- Publication Date: 2023
- DOI: [Publication DOI]
- Journal: [Journal Name]
Abstract
This research presents a novel approach to real-time hand gesture recognition using computer vision and deep learning techniques. The work focuses on improving the accuracy and efficiency of gesture detection systems for human-computer interaction. We propose an innovative implementation combining MediaPipe for hand landmark detection with a custom deep learning model for gesture classification.
Research Methodology
Dataset
The study utilized a comprehensive dataset including:
- Real-time hand gesture recordings
- Multiple hand orientations and positions
- Various lighting conditions
- Different skin tones and hand sizes
Technical Implementation
The system architecture consists of:
- MediaPipe hand landmark detection
- Custom neural network for gesture classification
- Real-time video processing pipeline
- Optimized GUI control system
Key Findings
- 98% accuracy in gesture recognition
- Less than 100ms processing latency
- Robust performance across different lighting conditions
- Successful integration with GUI control systems
Impact and Applications
The research findings have significant implications for:
- Touchless human-computer interaction
- Virtual and augmented reality systems
- Accessibility solutions
- Smart home control systems
- Interactive digital signage
Citation
@article{pati2023handgesture,
title={Real-time Hand Gesture Recognition Using Deep Learning},
author={Pati, Rishav Nath and [Other Authors]},
journal={[Journal Name]},
year={2023},
volume={},
pages={},
publisher={}
}
Future Research Directions
- Multi-hand gesture recognition
- Dynamic gesture sequence recognition
- Integration with 3D spatial tracking
- Cross-platform implementation
- Enhanced real-time performance
