BAV ONE Computer Vision Operating System · Turn existing cameras into programmable AI sensors
Explore the platformConvert tracked movement across calibrated lines or zones into entry, exit and occupancy analytics without promising unvalidated counting accuracy.
How it works
Each stage remains observable and can be validated independently.
A count is based on a tracked object crossing a calibrated boundary or meeting a zone rule, not on repeatedly counting detections in every frame.
Configured entry and exit logic can support current occupancy, directional flow and time-based operational views.
Camera height, field of view, occlusion, crowd density and route geometry influence results and must be validated at the site.
Capability, compatibility and performance depend on the camera scene, approved models, infrastructure and acceptance criteria. Human review remains appropriate for critical decisions.
Related guidance
Frequently asked questions
No. Accuracy depends on scene conditions, calibration and acceptance testing. Results should be presented with the validated operating limits.
Yes. BAV ONE supports assessed edge, on-premises, cloud and hybrid architectures. The selected design depends on camera access, compute, bandwidth, data-control and operational requirements.
No. Compatibility depends on stream access, protocol, resolution, frame rate, camera angle, lighting, network conditions and the selected use case. Bharat AI Vision validates these factors before rollout.
Begin with a camera and workflow assessment, then validate a focused one-to-four-camera pilot against agreed operating conditions and acceptance criteria.
Validate before rollout
Map the operating requirement, assess compatible streams and validate a focused workflow before expanding.
