Camera feeds contain more than motion. Geye is designed around the events your team needs to understand: where a queue forms, when a zone becomes busy, or what deserves a closer look.
Understand arrivals, occupancy, and queue formation without needing to identify individual visitors.
Example workflow: Define entry lines and service zones. Review aggregate counts alongside opening hours and staffing changes.
Validate in the pilot: Check occlusion, groups crossing together, and changing camera angles.
Surface possible PPE exceptions or activity inside a restricted zone for a trained operator to review.
Example workflow: Agree zone boundaries and visible PPE categories. Route a flagged event to an accountable reviewer.
Validate in the pilot: A detection is not a compliance judgment. Test lighting, uniforms, and partially obscured equipment.
Recognize dwell patterns and recurring congestion around work areas, loading bays, or service points.
Example workflow: Compare activity windows and annotate process changes before drawing conclusions.
Validate in the pilot: Separate process delays from camera outages and missed detections.
Highlight movement across a defined boundary during a relevant monitoring window.
Example workflow: Combine a zone crossing with schedule context and a human verification step.
Validate in the pilot: Account for weather, shadows, animals, and authorized access. Do not replace existing safety systems.
These 13 illustrations show possible applications, not live AI output or camera feeds. Any labels, boxes, or scores shown are illustrative, not measured accuracy. Potential security events require human verification and do not establish wrongdoing.
Explore aggregate visitor counts to support staffing and capacity planning without identifying individuals.
Review object volumes in a defined area and validate counts against manual samples.
Explore aggregate activity patterns to inform layouts and identify areas that may need more space.
Flag extended presence for contextual review. Waiting or remaining in an area is not evidence of wrongdoing.
Surface potentially unattended items so an operator can check ownership and context before responding.
Highlight possible personal protective equipment exceptions for a trained safety reviewer, not an automatic compliance verdict.
Flag potential zone entries and verify access permissions and schedules before taking action.
Surface activity that may warrant a vehicle security check. Human verification is required; an alert does not establish theft.
Bring potentially unusual motorcycle activity to an operator for verification, including authorized use and ownership context.
Flag a possible personal-belongings incident for human review. Proximity or gestures alone do not establish pickpocketing.
Explore potential personal-belongings alerts in busy markets, with crowd context and human verification before any response.
Present possible merchandise-related events for staff review. Handling a product is not proof of shoplifting.
Limit recognition to consent-based, authorized uses with restricted access and retention. Require human verification; not for mass tracking.
Review camera position, lighting, resolution, and the lawful purpose of collection. Prefer the least intrusive input that can answer the operational question.
Relate a detection to a zone and a time window. Tune thresholds using representative footage rather than assuming a model works equally well in every environment.
Give operators context, a review path, and a way to record false alerts. Set retention limits for any supporting images and restrict access by role.
Edge processing can reduce the need to move raw video off-site. It does not remove the need for secure configuration, hardware testing, storage planning, or operator training. Model throughput depends on the actual workload.
Geye / Visual intelligence