Many users migrating from software like (which has seen development slow or stop) are turning to CodeProject.AI for a "verified" future-proof solution. The advantages are clear:

When you implement a , a precise multi-stage filtering process takes over:

Each camera needs to be "verified" by the AI to filter its alerts:

Set the IP address to 127.0.0.1 (or localhost ) and change the network port to 32168 .

In the "To confirm" box, select and pick the objects you want to detect (e.g., person , car ). 4. Setup Local AI Inference

The combination of and Blue Iris is widely considered the gold standard for self-hosted, local computer vision in home security. It acts as a gatekeeper for your security cameras, verifying motion alerts by running them through artificial intelligence to ensure you only get notified for things that actually matter (like people, cars, or dogs) instead of shifting shadows or blowing leaves.

I can provide tailored configuration settings to maximize your system's processing speed. Share public link

CodeProject.AI + Blue Iris Verified: The Ultimate Guide to Local, Smart CCTV

Running local AI video analytics requires adequate processing power. While it can operate purely on a CPU, offloading computer vision tasks to a dedicated GPU or TPU drastically lowers latency.

To achieve stable, high-performance AI verification, your system needs to meet certain hardware and software requirements:

: If Blue Iris pertains to a surveillance or security application, verification could relate to the validation of its effectiveness, security, or compliance with specific standards.

In short, getting means moving from "motion is happening" to "a person is walking toward the front door ."

: In Blue Iris under Settings > AI , point the software to your CodeProject.AI server address (typically localhost:32168 ).