BAV ONE Computer Vision Operating System · Turn existing cameras into programmable AI sensors
Explore the platformAssociate detections over consecutive frames within one camera to understand movement without claiming cross-camera identity tracking.
How it works
Each stage remains observable and can be validated independently.
A temporary track identifier connects detections across frames in the same stream. It is not a person's identity.
Use validated classes and scene conditions to support trajectories, direction, dwell and line crossing.
Occlusion, crowding, camera motion and long absences can break tracks. Multi-camera identity continuity is not claimed here.
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. This page describes within-camera tracking. Cross-camera identity matching requires separate capabilities and governance.
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.
