OmnitrackAI Tracking Engine

A modular, real-time computer vision system using Python, MediaPipe, and FastAPI to track face landmarks, head movement (pitch/yaw/roll), and hand gestures. Results are streamed back in real-time over WebSockets to a premium HTML5/CSS/JS dashboard UI.

Features

• Face & Movement Tracking: Tracks face meshes and computes head orientation (Pitch, Yaw, Roll) using a PnP solver.
• Hand & Gesture Tracking: Detects hands, handedness, and classifies gestures (Fist, Open Palm, Peace, Thumbs Up, etc.).
• Real-Time WebSocket Pipeline: Processes webcam frames via WebSockets, allowing the application to run smoothly in remote/dockerized environments.

Prerequisites

• Python 3.9+
• Docker (Optional, for containerized running)
• Webcam (Required for tracking)

How to Run

Method 1: Local Setup

1. Create a virtual environment (recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the FastAPI server:

   python -m app.main

4. Open your browser and navigate to http://localhost:8000. Make sure to allow camera permissions.

Method 2: Docker Setup

1. Build the Docker image:

   docker build -t omnitrackai-tracker .

2. Run the Docker container:

   docker run -p 8000:8000 omnitrackai-tracker

3. Navigate to http://localhost:8000 in your browser.

Documentation

For more detailed information regarding the project's scope and verified tracking features, see the docs/project_scope.md folder.