VuePointSecure

Smarter Detection

AI Video Analytics That Filter Noise — and Catch What Matters

VuePointSecure's analytics layer combines best-in-class person, vehicle, and behavior models with per-camera tuning. Real threats reach operators in seconds; nuisance alerts get suppressed automatically.

CAM 01
PERSON97%
CAM 02
VEHICLE92%
CAM 03
PERSON88%
CAM 04
VEHICLE94%
CAM 05
PERSON91%
CAM 06
VEHICLE96%
Operator engaged · talk-down activeSJPD verified · 00:00:24

The Problem

Generic motion alerts are broken.

Off-the-shelf NVRs send thousands of motion alerts per night — branches, headlights, cats, rain. The signal-to-noise ratio is so bad that real events get missed. Property managers stop checking their app within a week.

  • Motion-only systems can't tell a person from a tree
  • False alerts cause operators (and police) to deprioritize sites
  • Generic AI rarely understands the layout of YOUR site
  • Cloud-only inference is slow and expensive at scale

The VuePointSecure Solution

Per-camera tuning, hybrid edge + cloud inference.

We tune each camera individually — masks, zones, sensitivity, time-of-day rules. Edge analytics filter cheap nuisance events; cloud analytics handle higher-fidelity person and vehicle classification. Operators only see verified events that match your rules.

  • Per-camera zones, masks, and schedules
  • Person, vehicle, loitering, line-cross, crowd detection
  • Edge inference saves bandwidth; cloud inference adds accuracy
  • Continuous retraining as your site evolves

What's included

Person & vehicle detection

High-accuracy models trained on real surveillance footage — not stock datasets.

Loitering & line-cross

Detects intent: someone hanging around the gate vs. walking past on the sidewalk.

Per-camera tuning

Each camera gets its own masks, zones, sensitivity, and time-of-day rules.

Multi-stage filtering

Edge analytics suppress cheap noise; cloud analytics confirm. Operators only see the real stuff.

Behavioral baselines

Models learn normal patterns at your site so unusual activity stands out.

Continuous improvement

Monthly review of misses and false alerts feeds back into tuning.

Process

How it works

  1. 1

    Camera inventory

    We pull a snapshot from every camera and identify zones, blind spots, and risk areas.

  2. 2

    Tuning sprint

    Initial masks, zones, schedules, and sensitivities go live within the first week.

  3. 3

    Live operation

    Operators monitor real-time events; tuning is refined continuously.

Outcomes our customers see

  • 90%+ reduction in nuisance alerts vs. motion-only systems
  • Faster operator engagement on real threats
  • Lower bandwidth and cloud costs
  • Better deterrence — events stop being missed

FAQ

Frequently asked questions

Will your analytics work with my brand of camera?

Yes if it's ONVIF/RTSP. We work with Hikvision, Dahua, Axis, Hanwha, Reolink, Avigilon, Bosch, and most others. We can also augment with our own edge-AI add-on units if your cameras lack analytics.

Is AI inference done on-camera, on a server, or in the cloud?

Hybrid. Cheap noise filtering runs at the edge; higher-fidelity classification runs in our cloud GPUs. We optimize for accuracy AND cost.

Can I see what AI flagged but the operator dismissed?

Yes. The dashboard surfaces all model events, including ones operators triaged as non-threats. This transparency is part of how we continuously retune.

How long until tuning is dialed in?

Most sites are 80% tuned in the first week and steady-state within 30 days. We never stop retuning as seasons and site usage change.

Ready to put real eyes on your site?

Free site assessment. Specialist reply within 1 business hour.

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