🌊 AI MARINE VISION & TELEMETRY SYSTEM

AquaPulse

Robust Computer Vision, BotSORT Object Tracking, Ensemble Kalman Filtering & Uncertainty Estimation for Aquatic Ecosystems

GitHub Repository ResearchGate Publication

🌊 Overview & Executive Summary

AquaPulse bridges multi-model YOLO neural vision, BotSORT multi-object tracking, Ensemble Kalman Filtering (EnKF) stochastic data assimilation, and local generative AI into an end-to-end ecosystem telemetry platform. By eliminating manual fish counting and enabling non-invasive automated observation in turbid aquatic environments, AquaPulse provides researchers, marine biologists, and environmental agencies with real-time ecological safety monitoring, extinction risk forecasting, and publication-ready LaTeX scientific reports.

📦 Downloads & Standalone Application

💻

AquaPulse Setup Installer

AquaPulse_Setup.exe

📁

Standalone Application Folder

AquaPulse_App/

🔬 Running from Source (Developer Mode)

1. Activate Virtual Environment:

c:\Users\parsa\Desktop\Code\venv\Scripts\Activate.ps1

2. Launch the Master AI Vision Telemetry System:

python "3 - AI process\main.py"

📊 System Telemetry & Output Session Structure

Each video analysis session generates an isolated, timestamped output bundle under video_analysis_sessions/:

video_analysis_sessions/<video_name>_<timestamp>/
├── output/           # Processed MP4 video with target reticles & EnKF HUD overlays
├── csv/              # Raw specimen counts and track data per frame
├── plots/            # 20 high-resolution analytical PNG telemetry plots
└── analysis/         # Ollama AI narrative report (.md), generated .tex file, and compiled PDF report

📄 Complete Project Documentation

For exhaustive mathematical details, system design diagrams, and empirical results, view:

1. Word Document Technical Report: 4 - Documentation/AquaPulse_Preprocessing_and_AI_Process_Report.docx

2. Interactive HTML Workflow & Architecture: 4 - Documentation/AquaPulse_System_Workflow_and_Structure.html

3. Markdown Architecture & Reference Spec: 4 - Documentation/AquaPulse_Workflow_and_Structure.md

🤝 Contributing

Fork the Repository on GitHub.
Create a Feature Branch: git checkout -b feature/amazing-feature
Commit Changes: git commit -m 'Add amazing feature'
Push to Branch: git push origin feature/amazing-feature
Open a Pull Request describing your changes.