Edge AI Security Guard with YOLOv8 on reComputer RK3576

Interduction

Traditional surveillance systems often rely on cloud services or dedicated servers, which can increase deployment complexity and cost. With the growing performance of edge AI devices, it is now possible to run real-time computer vision applications directly on local hardware.

Project Goal

In this project, I built an AI Security Guard using the Seeed Studio reComputer RK3576 AI Box, a USB camera, and the YOLOv8 object detection model.

The system continuously analyzes live video streams and detects people in real time. When a person is detected, it automatically:

• Counts the number of people
• Generates intrusion alerts
• Saves timestamped snapshots
• Records detection events
• Streams the processed video through a web browser

The entire AI inference pipeline runs locally on the RK3576 without requiring cloud connectivity.

Why reComputer RK3576?

The reComputer RK3576 provides a powerful platform for deploying edge AI applications while maintaining low power consumption and compact size.

Key advantages include:

• ARM-based AI computing platform
• Linux operating system support
• Camera connectivity
• Local AI inference capability
• Suitable for always-on edge deployment

For lightweight object detection tasks such as YOLOv8n, the platform offers an excellent balance between performance and efficiency.

System Workflow

The workflow of the AI Security Guard is straightforward:

• Capture video frames from a USB camera.
• Process each frame using the YOLOv8 model.
• Detect people and calculate the current count.
• Overlay detection results on the video stream.
• Generate alerts when a person is detected.
• Save timestamped snapshots automatically.
• Record events into a log file.
• Stream the processed video to a web browser using Flask.

The entire process runs continuously on the RK3576 device.

Real-Time Detection

This project demonstrates how the reComputer RK3576 can be used to build a lightweight and practical edge AI surveillance solution for homes, offices, smart retail environments, and educational demonstrations.

Below is the live monitoring interface running on the RK3576.

Event Logging

Whenever a person is detected, the system records the event and stores a timestamped log entry for future review.

Browser-Based Monitoring

To simplify deployment, a lightweight Flask server was added.

Instead of relying on a local display, the system streams processed video directly to a browser.

Users can access the monitoring interface from any device on the same network:

http://127.0.0.1:5000/

The web interface displays:

• Live camera feed
• Detection bounding boxes
• Person count
• Alert status

This approach is particularly useful for headless edge devices.

Results

The final system successfully performs:

• Real-time human detection
• Person counting
• Intrusion alert generation
• Automatic snapshot storage
• Event logging
• Browser-based monitoring

All processing is executed locally on the reComputer RK3576, demonstrating the practicality of deploying computer vision applications at the edge.

Conclusion

This project shows how a compact edge AI platform can be used to build a practical smart surveillance system with minimal hardware requirements.

By combining the reComputer RK3576, YOLOv8, OpenCV, and Flask, it is possible to create an intelligent monitoring solution that operates entirely on-device while providing real-time insights through a simple web interface.

The project can serve as a foundation for more advanced edge AI applications, such as facial recognition, anomaly detection, smart access control, or IoT-integrated security systems.