Development of a Gesture Recognition System for Human-Computer Interaction
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Background<br>
- 1.2Objectives<br>
- 1.3Scope<br>
- 2.Literature Review<br>
- 2.1Gesture Recognition Techniques<br>
- 2.2Computer Vision for Hand Gesture Analysis<br>
- 2.3Deep Learning Models for Gesture Recognition<br>
- 3.System Design and Architecture<br>
- 3.1Image Acquisition and Preprocessing<br>
- 3.2Feature Extraction and Representation<br>
- 3.3Gesture Classification Models<br>
- 3.4Real-time Gesture Detection<br>
- 4.Implementation<br>
- 4.1Dataset Collection and Annotation<br>
- 4.2Model Training and Optimization<br>
- 4.3Real-time Gesture Recognition Interface<br>
- 5.Testing and Evaluation<br>
- 5.1Performance Metrics<br>
- 5.2Usability Testing<br>
- 5.3Robustness and Error Analysis<br></p>
Project Abstract
<p> This project focuses on the development of a gesture recognition system that enables natural and intuitive interaction between users and computing devices. The system will utilize computer vision techniques, deep learning algorithms, and real-time image processing to accurately detect and interpret hand gestures for controlling applications, games, and virtual environments. The project will explore different gesture recognition approaches, optimize model performance, and evaluate the system's usability and robustness in various scenarios. <br></p>Project Overview
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