Edge vs Cloud AI for Workplace Safety Cameras

What is the difference between edge and cloud AI for workplace safety cameras?

The difference is where the decision is made. DHI decides on a node at your site, camera by camera, so raw video stays on-premises by default. A cloud pipeline sends each frame out to a service for analysis and waits for the result to travel back before anything can act on it.

How does latency differ?

Detector inference plus tracking and rule evaluation runs at 30 ms median inference per camera. Camera to alert is longer: detectors sample each camera every 1.5 seconds by default, and that sampling interval, not inference, is the dominant term. DHI does not offer a latency guarantee. Pilots should validate camera-to-alert on the actual network, VMS workflow, and alert destination.

Where does the video go?

With DHI, raw video stays on your premises by default. Inference happens at the edge, and only structured safety events or policy-approved clips are transmitted off-site when your workflow allows it.

What happens when the internet drops?

DHI's edge-native architecture means inference and event detection happen locally. The system can continue detecting events during internet interruptions, with remote management and external routing depending on the configured deployment.

What does edge processing cost you?

Edge processing means deploying and maintaining compute at the camera rather than renting it by the API call. DHI runs on any GPU edge node, from a small 8 GB edge module to an industrial x86 server, and you size the node to the number of cameras.

What stays in place?

DHI reads the RTSP or ONVIF streams from the cameras you already run and runs alongside your existing video management system. There is nothing to rip out or replace.