Human motion tracking and gesture recognition
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Significance<br> 1.2 Objectives of the Project<br>2. Human Motion Tracking Technologies<br> 2.1 Depth Sensing and 3D Reconstruction<br> 2.2 Pose Estimation and Skeleton Tracking<br>3. Gesture Recognition Algorithms<br> 3.1 Feature Extraction and Representation<br> 3.2 Machine Learning Models for Gesture Classification<br>4. Sensor Integration and Data Acquisition<br> 4.1 Depth Cameras and Inertial Measurement Units (IMUs)<br> 4.2 Data Synchronization and Calibration<br>5. Real-time Motion Tracking and Gesture Detection<br> 5.1 System Architecture and Software Framework<br> 5.2 Performance Optimization and Latency Reduction<br></p>
Thesis Abstract
<p> This project aims to develop a system for human motion tracking and gesture recognition using computer vision and machine learning techniques. The project will focus on capturing and analyzing human movements in real-time to recognize gestures and interactions for various applications, such as virtual reality, gaming, and human-computer interaction. The project will explore the use of depth sensors, cameras, and wearable devices for accurate motion tracking and gesture recognition. <br></p>Thesis Overview
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