Many organizations already have CCTV or IP camera infrastructure in place. The question is whether that infrastructure can support AI video analytics without a full replacement. The answer depends on the camera, the stream it exposes, and the operating environment.
What makes a camera compatible?
Compatibility is not a universal property. A camera assessment evaluates the stream protocol (typically RTSP), resolution, frame rate, codec, camera angle, lighting conditions, and network path. Cameras that expose accessible RTSP streams at adequate resolution and frame rate are candidates for AI integration.
How BAV Edge connects to existing cameras
BAV Edge is the edge connectivity and inference layer of BAV ONE. It connects to compatible RTSP streams, manages stream health, and runs approved AI models locally. This means video does not need to travel to a remote cloud service for inference to happen.
From stream to operational event
Once a stream is connected, BAV ONE applies detection models, within-camera tracking, and Vision Policies. A Vision Policy evaluates the detection against configured zones, schedules, duration and severity before creating an operational event. This means not every detection becomes an alert.
What to do before integration
Start with a camera and workflow assessment. Document each camera's stream endpoint, credentials, resolution, angle, and intended use case. Validate that the network path from the camera to the edge device is accessible. Define the operating condition and acceptance criteria before enabling inference.
Deployment options
Processing can run at the edge for local execution, on-premises for site-controlled infrastructure, or in a hybrid design where edge inference feeds selected events to central services. The right architecture depends on bandwidth, data-control requirements, and operational needs.