Ayonix Video Analytics

Industry

Video analytics for education estates and campus security

In short

Education video analytics covers entrance counting, building and space utilisation, and out-of-hours perimeter and restricted-area detection across a campus estate. Classrooms, teaching spaces and any form of student behaviour monitoring are excluded by design; the value lies in estates utilisation evidence and in out-of-hours site security where buildings are empty.

Manager reviewing factory, retail, transit and parking sites in an operations centre
Conceptual illustration of multi-site operational oversight. AI-generated conceptual image, not a customer deployment.

The problem

What education teams are dealing with

  • Out-of-hours break-ins and vandalism are discovered the next morning from the recording.
  • Teaching and study space utilisation is unknown, so estates decisions are argued from timetable assumptions.
  • Campus buildings have many entrances and no data on which are actually used.
  • Car parks fill at term start and there is no live availability information.
  • Any camera proposal in an education setting rightly attracts safeguarding and privacy scrutiny.

Camera placement

Where cameras need to be, and why

Camera position decides more of the outcome than any software setting. These are the placements that matter in this sector.

Recommended camera placement forEducation
Location Purpose What matters
Site perimeter and boundary Out-of-hours intrusion Rules active only outside term-time operating hours; schedules do most of the work here.
Building entrances Directional counting Overhead placement at the threshold; counting is aggregate and identifies nobody.
Study and common spaces Occupancy derivation Doorway counting rather than in-room monitoring keeps the intrusion minimal.
Plant rooms and IT spaces Out-of-permit presence Schedule-aware rules aligned to maintenance windows.
Campus car parks Area occupancy Standard elevated placement for area-level reporting.

Workflow

From event to acknowledged action

Event workflow

  1. 1 Estate cameras are ingested; teaching spaces are excluded from the analytics configuration entirely.
  2. 2 People and vehicles are detected in permitted areas only.
  3. 3 Counting and occupancy rules run to the term and daily schedule.
  4. 4 Perimeter and restricted-area rules activate outside operating hours.
  5. 5 Utilisation series publish to estates; security events route to the site team.
  6. 6 Evidence access is role-restricted and audit-logged.

Operator workflow

  1. 1 Site security receives an out-of-hours perimeter event with a clip.
  2. 2 The camera is opened from the alert and the response follows the campus procedure.
  3. 3 Estates reviews building and space utilisation against the timetable.
  4. 4 Car park occupancy is published to campus signage at term start.
  5. 5 Safeguarding lead reviews the configuration and access logs on the agreed cycle.

Operations

Dashboards, integration and deployment

Dashboard metrics

  • Out-of-hours perimeter events and acknowledgement time
  • Entrance counts by building and interval
  • Space utilisation against timetabled capacity
  • Car park occupancy by area
  • Restricted-area events outside permit windows
  • Camera availability

Integration options

  • REST and webhook delivery into estates and timetabling systems
  • Campus signage integration for parking availability
  • VMS alarm delivery for out-of-hours security events
  • ONVIF and RTSP ingestion from the campus camera estate
Compatibility states →

Deployment options

  • On-premise processing inside the institution network
  • Edge processing for outlying buildings
  • Multi-site management across a campus or trust estate
  • Air-gapped deployment where required by institutional policy
Compare architectures →

Evidence

Deployment pattern

Campus estate pattern

Out-of-hours perimeter detection, building entrance counting and doorway-derived space utilisation, with teaching spaces excluded by design.

Utilisation and detection performance are measured on your own campus. No cross-institution benchmarks are published.

This is an anonymised pattern describing how a deployment of this kind is structured. It is not a named customer reference. No customer names, logos, testimonials or measured results are published on this site, because none have been supplied for publication. More on how evidence is handled .

Governance

Privacy and governance in this sector

  • Classrooms, teaching spaces, changing areas and any form of student behaviour monitoring are excluded by design.
  • All outputs are aggregate; no student or staff member is identified.
  • Safeguarding and data protection review should precede deployment, and the excluded-areas list should be documented and published internally.
  • Staff-area measurement engages employee monitoring obligations and consultation duties.
  • Perimeter rules should be schedule-limited to out-of-hours periods so that normal campus life is not monitored.

Frequently asked questions

Will this monitor students?

No. Teaching spaces are excluded, perimeter rules are limited to out-of-hours periods, and all outputs are aggregate counts and occupancy with no identity. Student behaviour monitoring is not part of this pattern and is not offered.

Can it measure how study spaces are used?

Yes, through doorway counting that derives occupancy without monitoring inside the room. That answers the estates utilisation question with the least intrusive method available.

What approvals are needed?

Typically safeguarding and data protection review, plus staff consultation where staff areas are in scope. Documenting the excluded areas explicitly is usually what makes the approval straightforward.

Other industries

Author
Gabriel Bamola, Chief Marketing Officer, Ayonix
Technical review
Dr Sadi Vural, Founder and Chief Executive Officer, Ayonix
Published
Last reviewed

Design a education pilot around your own cameras

Start with a camera readiness review and a log-only baseline, then measure against acceptance criteria we agree before the pilot begins. That is what turns an analytics evaluation into a decision.