When designing real-time surveillance systems for high-risk security environments, traditional cloud-based logging presents two major issues: latency delays over weak networks and the vulnerability of databases that can be tampered with or deleted by intruders.

To solve this, I designed and built an edge-AI automated surveillance pipeline that detects local threats and processes video frames at the edge using TensorFlow Lite on a Raspberry Pi 4B and automatically records tamper-proof attack logs directly to the Ethereum Sepolia Testnet using Web3.py .

Here is a breakdown of how the system was built, deployed, and tested.

The Architecture

The system consists of three main components working together:

Edge Inference Engine (AI/IoT): OpenCV and TensorFlow Lite processing live camera frames directly on a Raspberry Pi 4B.

Web Monitoring Dashboard: A real-time web interface built with HTML/JS and hosted on Render.

Immutable Audit Layer (Blockchain): A Solidity smart contract deployed on the Ethereum Sepolia…