Solution-Hackathon

🚨 AI Vision Safety Hub

Automated CCTV Anomaly Detection & Crisis Routing for Hospitality Venues

Built for the Solution Challenge 2026 - Build with AI, this prototype transforms passive CCTV cameras into active sentinels. Using real-time computer vision, the system monitors video feeds for physical anomalies (like slip-and-falls) and leverages Google’s Gemini API to instantly generate context-aware, localized emergency alerts.

---

🌟 Key Features

• Zero-Latency Anomaly Detection: Uses skeletal tracking to detect rapid vertical drops indicative of a fall or injury.
• Context-Aware Escalation: Integrates with the Google Gemini API (gemini-1.5-flash) to generate professional, situation-specific emergency dispatch alerts.
• Live Incident Dashboard: A clean, dark-mode web interface built with Streamlit for security personnel to monitor feeds and view incoming alerts.
• Hardware Agnostic: Processes standard video formats, allowing it to act as an intelligent layer over existing hotel CCTV infrastructure.

---

🛠️ Tech Stack

• Frontend & Backend: Streamlit (Python)
• Computer Vision: OpenCV & Google MediaPipe (Pose Tracking)
• Generative AI: Google Gemini API
• Data Processing: NumPy

---

📂 Project Structure

├── app.py                 # The main Streamlit dashboard and application logic
├── fall_detector.py       # Custom computer vision model utilizing MediaPipe Pose
├── requirements.txt       # Python dependencies required to run the project
└── README.md              # Project documentation

🚀 Getting Started

Follow these instructions to set up and run the AI Vision Safety Hub on your local machine.

Prerequisites

• Python 3.8 or higher installed on your system.
• A valid Google Gemini API Key. You can get one from Google AI Studio.
• A sample CCTV video clip (e.g., .mp4) to simulate a camera feed.

Installation

1. Clone the repository:

   git clone [https://github.com/YOUR_USERNAME/ai-vision-safety-hub.git](https://github.com/YOUR_USERNAME/ai-vision-safety-hub.git)
   cd ai-vision-safety-hub
   

2. Create a virtual environment (Recommended):

   python -m venv venv
   
   # On Windows:
   venv\Scripts\activate
   
   # On macOS/Linux:
   source venv/bin/activate
   

3. Install the dependencies:

   pip install -r requirements.txt
   

💻 Usage

1. Start the application: Ensure your virtual environment is active, then run:

   streamlit run app.py
   

2. Configure the Dashboard:
   • The app will automatically open in your default web browser (usually at http://localhost:8501).
   • In the sidebar, paste your Gemini API Key.
   • Upload a sample CCTV video file.
3. Simulate:
   • Watch the live feed as the AI draws skeletal landmarks. When a fall is detected, the system will pause briefly to query the Gemini API and output a high-priority red alert on the dashboard.

🔮 Future Development

• Multi-Camera Spatial Mapping: Tracking an emergency as it moves across different camera zones in a large resort.
• Direct EMS Integration: Automatically forwarding critical Gemini alerts directly to local ambulance or fire dispatch systems.
• Expanded Threat Detection: Integrating YOLOv8 to detect environmental hazards like smoke, fire, or unattended baggage.

---

Team Arkz | Solution Challenge 2026