CCTV and Mojo AI for occupational safety monitoring

1. System Architecture

Key Components:

1. CCTV Cameras:
   • Installed at critical locations (entry points, work areas, warehouses, hallways).
   • Continuously provide video data for analysis.
2. Edge AI Devices:
   • Hardware placed on-site (e.g., edge servers or AI-integrated IoT devices).
   • Run machine learning models to analyze video from CCTV without needing to send data to the cloud.
3. Factory Local Network:
   • Connects CCTV and Edge AI for fast data transmission.
   • Minimizes latency in processing.
4. Central Management Software - MOJO AI VISION STUDIO :
   • A management interface displaying notifications, reports, and live video streams.
   • Can store data locally or integrate with cloud systems.
5. Cloud Server (Optional):
   • Stores data for long-term purposes and provides advanced analysis capabilities if needed.
   • Synchronizes data with edge devices.

2. How It Works

Detailed Process:

1. Data Collection:
   • CCTV cameras capture real-time video.
   • Video data is sent directly to Edge AI devices via the local network.
2. Real-Time Analysis on Edge AI:
   • Edge AI processes video data to:
     • Detect safety violations (e.g., absence of helmets, reflective vests, or unauthorized entry into restricted areas).
     • Identify abnormal behaviors (e.g., falls, collisions, or unsafe movements).
     • Verify the correct usage of Personal Protective Equipment (PPE).
   • Uses AI models such as YOLO, OpenPose, or advanced image recognition algorithms.
3. Alerts and Actions:
   • When a violation is detected, the system immediately triggers alerts:
     • Alarms or warning lights on-site.
     • Notifications via email, SMS, or management applications to the safety team.
   • Logs incidents for later analysis.
4. Storage and Reporting:
   • Analysis results and video footage are stored in:
     • Local memory on edge devices (short-term).
     • Cloud servers (long-term, if needed).
   • Generates periodic reports on safety compliance and incident frequency.
5. Integration with Management Systems:
   • The system connects to other platforms such as ERP or HSE (Health, Safety, Environment) systems to synchronize data and enhance management efficiency.

3. Model Diagram

1. CCTV Cameras
   ↓ (Transmit video via the local network)
2. Edge AI Devices (Real-time analysis)
   ↓
3. Alerts: Sent to on-site systems and CMS
   ↓
4. Central Management System (Local or Cloud storage)
   ↔ (Integrates with other systems)

4. Advantages of the Model

1. Quick Response:
   • Processes data on-site, reducing latency compared to cloud-based systems.
2. High Security:
   • Data doesn’t need to be entirely sent to the cloud, reducing the risk of leaks.
3. Optimized Bandwidth:
   • Only sends critical information or alerts, saving network resources.
4. Scalability:
   • Easily add more cameras or edge devices if the factory expands.
5. Lower Long-Term Costs:
   • Reduces storage and processing fees on the cloud.

5. Specific Applications in Factories

1. PPE Violation Detection:
   • Checks if workers are wearing helmets, safety glasses, or reflective vests.
2. Monitoring Hazardous Areas:
   • Detects unauthorized entry into restricted or unsafe zones.
3. Behavior Analysis:
   • Identifies abnormal behaviors like slips, falls, or standing in unsafe positions.
4. Headcount Management:
   • Ensures the number of people in a workspace doesn’t exceed the limit.

6. Related Technologies

• AI Models: TensorFlow, OpenCV, PyTorch, Mojo AI Vision
• Edge AI Devices: Nvidia Jetson, Google Coral, Raspberry Pi
• Storage Systems: Hybrid Cloud (AWS, Azure) or on-premises systems.