Dwell-Time Logic
Measure how long a valid within-camera track remains in a configured area and compare it with the approved threshold.
BAV ONE Computer Vision Operating System · Turn existing cameras into programmable AI sensors
Explore the platformBAV ONE Computer Vision Operating System · Turn existing cameras into programmable AI sensors
Explore the platformDwell is a time-and-zone measurement. Business policy—not the model—determines whether that duration matters.
How it works
Each stage remains observable and can be validated independently.
Measure how long a valid within-camera track remains in a configured area and compare it with the approved threshold.
Apply schedule, object class, location and operational purpose before generating an event.
Normal waiting, working or queueing must not automatically be labeled suspicious. Human review and site policy remain important.
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. Dwell only describes duration in a configured zone. The business context and approved policy determine whether action is appropriate.
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.