{"id":25371257,"url":"https://github.com/dearabhin/ai-surveillance-system","last_synced_at":"2026-02-16T18:32:36.263Z","repository":{"id":263231479,"uuid":"889744408","full_name":"dearabhin/AI-Surveillance-System","owner":"dearabhin","description":"The AI Beach Surveillance System is designed to monitor public beaches and ensure safety by detecting people crossing predefined danger zones. 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The system ensures safety by detecting people crossing predefined boundaries (like a sea or danger zone) and sending real-time alerts to prevent potential dangers.\n\n---\n\n## ✨ Features\n- **Real-Time Video Monitoring**: Tracks people on the beach and their proximity to the danger zones.\n- **Boundary Detection**: Uses AI models to detect if people cross safety limits.\n- **Alerts**: Automatically sends email alerts when a person enters the danger zone.\n- **User Interface**: A simple web-based interface to start and stop monitoring.\n\n---\n\n## 🛠️ Technology Stack\n- **Backend**: Python, Flask\n- **Frontend**: HTML, CSS, JavaScript\n- **AI Models**: YOLOv8 for object detection\n- **Other Tools**: OpenCV for video processing, SMTP for sending alerts\n\n---\n\n## 🚀 Getting Started\n\n### 1. Clone the Repository\n```bash\ngit clone https://github.com/dearabhin/AI-Beach-Surveillance-System.git\ncd AI-Beach-Surveillance-System\n```\n\n### 2. Install Dependencies\nEnsure you have Python 3.8+ installed. Then, install the required Python packages:\n```bash\npip install -r requirements.txt\n```\n\n### 3. Set Up Email Alerts\nUpdate the `alert_system.py` file with your email credentials:\n- `sender_email`: Your email address\n- `receiver_email`: The email to send alerts\n- `password`: Your email password (or app password if using Gmail)\n\n### 4. Run the Application\nStart the Flask server:\n```bash\npython src/app.py\n```\nAccess the web app at `http://127.0.0.1:5000`.\n\n---\n\n## 📂 Project Structure\n\n```\nAI-Beach-Surveillance-System/\n│\n├── data/                   # Training data for AI models\n├── models/                 # Pre-trained and fine-tuned models\n├── src/                    # Source code\n│   ├── app/                # Flask application\n│   │   ├── static/         # Frontend assets (CSS, JS, images)\n│   │   ├── templates/      # HTML templates\n│   │   ├── app.py          # Main Flask app\n│   │   ├── monitoring.py   # Real-time monitoring logic\n│   │   ├── detection.py    # AI-based detection\n│   │   ├── alert_system.py # Email alert system\n│   │   └── utils.py        # Helper functions\n├── requirements.txt        # Python dependencies\n├── README.md               # Project documentation\n└── LICENSE                 # License file\n```\n\n---\n\n## 🌟 How It Works\n1. The system starts live monitoring using the `monitoring.py` script.\n2. **Object Detection**: The YOLOv8 model detects people in the video feed.\n3. **Boundary Monitoring**: The system tracks the position of detected people relative to the danger zone boundary.\n4. **Alerts**: When someone crosses the boundary, the `alert_system.py` script sends an email alert.\n\n---\n\n## 📸 Screenshots\n\n### Home Page\n![Home Page](static/images/home_page_screenshot.png)\n\n### Monitoring in Action\n![Monitoring](static/images/monitoring_screenshot.png)\n\n---\n\n## 💻 Deployment\n\n### Local Deployment\nFollow the [Getting Started](#getting-started) steps to run the app locally.\n\n### Cloud Deployment\nYou can deploy the application on platforms like:\n- **Heroku**\n- **AWS EC2**\n- **Google Cloud Platform**\n- **Microsoft Azure**\n\n---\n\n## 🛡️ License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\n## 🙋‍♂️ Contributing\nContributions are welcome! Feel free to fork the repository and submit a pull request.\n\n---\n\n## 📧 Contact\nFor any queries or feedback, reach out to **abhinkrishna.com**.\n\n---\n\n## 🌍 Acknowledgments\nSpecial thanks to:\n- [YOLOv8](https://github.com/ultralytics/yolov8) for the powerful object detection model.\n- OpenCV for real-time video processing.\n- Flask for a lightweight backend framework.\n```\n\nThis file is formatted for Markdown and is suitable for direct upload to a GitHub repository. It includes proper sectioning, code blocks, and formatting for better readability. 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