BAV ONE Computer Vision Operating System · Turn existing cameras into programmable AI sensors
Explore the platformA technical path for camera onboarding, secure stream access, edge execution, detection publishing, operational events and enterprise integration.
How it works
Each stage remains observable and can be validated independently.
Document the camera identifier, stream endpoint, credentials, codec, resolution, frame rate, orientation and intended use before enabling inference.
RTSP is a common stream path. ONVIF discovery or configuration and NVR/VMS integration are used only where the device, system and approved deployment support them.
Store camera credentials through approved secret handling, assign the camera to BAV Edge and verify local connectivity without exposing private camera networks to the public internet.
Assign only validated models and classes, then monitor decode, inference, tracking and stream health independently so one metric is not substituted for another.
Detections and tracks become operational events only after configured zones, lines, schedules, duration and Vision Policies are evaluated.
Approved APIs and webhooks can publish events to enterprise systems. Health monitoring should separate camera reachability, stream availability, inference status and integration delivery.
Validate DNS, routing, TLS, firewall, proxy, credentials, clock synchronization and certificate trust. Keep camera networks, Redis and internal services within approved boundaries.
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
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.
