BAV ONE Computer Vision Operating System · Turn existing cameras into programmable AI sensors
Explore the platformApply explicit virtual boundaries and site rules to compatible outdoor camera views while retaining human-led response for critical events.
How it works
Each stage remains observable and can be validated independently.
Define polygons or lines within the calibrated camera scene rather than treating the entire image as one security condition.
Use tracked direction, object class, time windows and duration to reduce irrelevant events.
BAV Edge can run approved inference near remote or controlled sites and share policy-approved events and evidence.
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. It can support earlier awareness and evidence for configured conditions. Physical security and human response remain separate responsibilities.
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.
