Human motion tracking and gesture recognition
Table Of Contents
1. Introduction
1.1 Background and Significance
1.2 Objectives of the Project
2. Human Motion Tracking Technologies
2.1 Depth Sensing and 3D Reconstruction
2.2 Pose Estimation and Skeleton Tracking
3. Gesture Recognition Algorithms
3.1 Feature Extraction and Representation
3.2 Machine Learning Models for Gesture Classification
4. Sensor Integration and Data Acquisition
4.1 Depth Cameras and Inertial Measurement Units (IMUs)
4.2 Data Synchronization and Calibration
5. Real-time Motion Tracking and Gesture Detection
5.1 System Architecture and Software Framework
5.2 Performance Optimization and Latency Reduction
Thesis Abstract
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.
Thesis Overview
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