Dec 30, 2024●15 reads●No License

pose-estimation

#PoseEstimation #ComputerVision

b
@bashitalishaikh

Real-Time Pose Estimation for Augmented Reality Gaming

Description

This project leverages Computer Vision and Deep Learning to perform real-time pose estimation for augmented reality (AR) gaming. By tracking and analyzing human body movements, the system can enhance gaming experiences by enabling precise interactions with virtual environments.

Features:

Detects and tracks key human body points (e.g., joints).
Real-time processing for seamless integration into AR applications.
High accuracy and efficiency using state-of-the-art models like OpenPose or Mediapipe.
Customizable for gaming environments and fitness applications.

Documentation

1. Prerequisites

Install necessary libraries:

pip install opencv-python mediapipe numpy matplotlib

2. Running the Project

Clone the repository:

git clone https://github.com/bashitalishaikh/pose-estimation-ar-gaming.git

Navigate to the project folder:
```
cd pose-estimation-ar-gaming
```
Run the main script:
```
python pose_estimation.py
```

3. Key Components

Pose Detection Module: Detects key points on the human body in real time.
AR Integration Module: Maps body movements to AR game controls.
Visualization: Displays pose estimation overlays on video input.

4. Models Used

Mediapipe Pose: Lightweight and fast for real-time applications.
OpenPose: High accuracy for detecting multiple poses.

Future Aspects

Enhanced Interactivity:
- Introduce gesture-based controls for AR games.
- Integrate voice commands for an immersive experience.
Scalability:
- Adapt for multiplayer environments.
- Extend for motion capture in fitness and healthcare.
Optimized Performance:
- Utilize GPU acceleration for faster processing.
- Develop mobile-friendly versions for AR gaming on smartphones.
Application Domains:
- Fitness tracking and personalized workout recommendations.
- Virtual reality (VR) integration for immersive experiences.