Deploying a Low-Latency Surveillance Pipeline: An Architecture Guide
"Real-time" is one of those phrases that gets used loosely, and in a video analytics pipeline it hid …
Choosing a GPU for Real-Time Video Analytics
Capacity planning tells you how many GPUs a deployment needs. This is the other half of the question …
GPU Capacity Planning for Real-Time AI Video Analytics
Ask a vendor how many cameras their AI runs on one GPU and you will usually get a single confident n …
On-prem vs Cloud for Real-Time AI Video Analytics
For most software written this decade, the cloud is the default and running your own hardware needs …
Camera Edge Processing vs Server-Based Processing: The Real Trade-offs
There are two honest places to run AI on a video feed, and the choice between them shapes almost eve …
Camera-Agnostic AI Video Analytics: Add AI to the Cameras You Already Own
The most consequential decision in a central video analytics system is one that rarely makes it into …
Network and Bandwidth Design for Large Camera Fleets
The GPU gets all the attention in a real-time video analytics deployment, and the network quietly ru …
Pulling Live Streams From Your VMS into AI: Integration Patterns That Work
Adding AI to an existing camera estate comes down to one unglamorous problem: getting a copy of the …
Scaling Live Surveillance to Thousands of Cameras: What Actually Breaks
Scaling a real-time video analytics deployment is not one smooth curve where everything simply gets …
