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Security camera feeding a multi-view wall with detection overlays in video analytics software

Video Analytics Software That Decides at the Camera

Aeye Systems runs custom computer vision on NVIDIA Jetson hardware inside vehicles and fixed installations, matches detections against live watchlists on the spot, and escalates into a command center. Recognition keeps working when the connection does not, which is the whole reason the model sits at the edge.

What Video Analytics Software Solves

Where camera footage stops turning into anything anyone can act on. The cameras record, the storage fills, and a review happens only after something has already gone wrong. The value sits in the minute between the event and somebody noticing it.

Nobody Watches the Footage Until It Is Too Late

Nobody Watches the Footage Until It Is Too Late

Cameras record continuously and the material is reviewed after an incident, so the system documents what happened.

  • Detections matched against live watchlists at the moment they occur
  • Configurable alert types and severity so attention goes to what warrants it
  • Escalation into a command center with the context attached
The Connection Cannot Be Trusted

The Connection Cannot Be Trusted

Vehicles, remote sites and industrial floors lose the network, and a cloud-only pipeline stops recognising anything.

  • Models running on hardware at the point of capture
  • Local decisions that survive an outage, with results synced when the link returns
  • Bandwidth spent on results and clips that matter
Evidence Falls Apart Under Review

Evidence Falls Apart Under Review

A detection with no chain of custody, no timestamp integrity and no export path is worth nothing when it is questioned.

  • An evidence review and export layer built for agency scrutiny
  • Immutable records of what was detected, when, and by which model version
  • Historical search across events with the source material attached
Accuracy Drops After Deployment

Accuracy Drops After Deployment

The model performed in testing and degrades in the field, where lighting, angles and weather look nothing like the training set.

  • Footage you already hold labelled and folded into the training set
  • Accuracy measured against a held-out set before a model reaches production
  • Drift monitoring that catches degradation before an operator reports it

What Makes Video Analytics Software Hard to Build

Running a model on a clip is a solved problem. Running it on constrained hardware, in weather, on a network that disappears, with an output somebody will challenge in a hearing, is the engineering. Hygge decides where inference runs, what the hardware can carry and how evidence is recorded during the audit, because those three answers set everything built afterwards.

What Makes Video Analytics Software Hard to Build

Where AI Video Surveillance Software Runs

Sectors where a camera already exists and nobody is watching it, and where Hygge has shipped it. What changes between them is what has to be spotted: a safety breach, a queue, a plate, an empty shelf. Computer vision development services follow the same spine in all of them, and the detection targets move with the site.

All Hygge Industries
Public Safety & Security

Public Safety & Security

Vehicle and fixed installations matching detections against live watchlists, with evidence built for agency review.

Public Safety Software
Real Estate & PropTech

Real Estate & PropTech

Construction and property progress read from site photographs and turned into a score investors can act on.

PropTech Software
Logistics & Warehouse Automation

Logistics & Warehouse Automation

Warehouse and industrial floors where a camera watches a process that cannot stop.

Logistics Software Development
Retail & E-Commerce

Retail & E-Commerce

Retail environments measuring what happens in front of a shelf or a window.

Retail Software Development
Aviation

Aviation

Aviation ground operations where visual checks carry a record.

Aviation Software Development

The Stack Behind Video Analytics Platform Development

Video analysis costs money per frame, so this stack is chosen to run recognition where the camera is and send back only what matters. Storage and retrieval are built for the review that happens weeks later.

Models running on the hardware at the point of capture, sized to its compute and thermal envelope.

NVIDIA JetsonNVIDIA Jetson
NVIDIA DeepStreamNVIDIA DeepStream
TensorRTTensorRT
ONNX RuntimeONNX Runtime
NVIDIA Triton Inference ServerNVIDIA Triton Inference Server

Video Analytics Software Track Record

Company numbers across every project, from the first audit through the years a system stays in service. Computer vision software development is judged on false positives, because an alert nobody trusts gets switched off in week three. The audit at the start is what makes the delivery date and the price hold.

7 years
Shipping production software for US and European companies
1,000,000
Users on platforms Hygge built and migrated
3 months
From kickoff to pipelines running on a schedule
1 week
The longest you ever wait for a working build you can open and try

How a Video Analytics Platform Gets Built

Hardware and accuracy first, because both are expensive to change later. A pilot runs against your own footage until the false positive rate is one your team will tolerate. Rollout follows once that number is agreed.

  1. Site and hardware audit

    2 weeks

    Hygge checks the cameras and compute already deployed, the network at each site, and what has to be recognised. The output is a hardware plan, a scope and an exact price.

  2. Model and accuracy baseline

    3-5 weeks

    Footage you already hold gets labelled, models trained for the environment, and accuracy measured against a held-out set before anything is deployed.

  3. Edge deployment and alerting

    4-6 weeks

    Inference running on device at a pilot site, matching against live lists, with alert types, severity and escalation wired in.

  4. Command interface and evidence

    3-5 weeks

    Live map, historical search and an evidence review and export layer, then rollout across remaining sites.

Related CCTV Analytics Software Work

Projects where a camera feed had to produce a decision somebody acts on. Each started with footage already being recorded and nobody reviewing it in time. What you see is the system that closed that gap and the numbers that moved.

All Hygge Case Studies

Video Analytics: Frequently Asked Questions

Vision projects are judged on accuracy and on evidence that holds up. Start here. AI video surveillance software and cctv analytics software describe the same build in the words of two different buyers. AI video surveillance software and cctv analytics software describe the same build in the words of two different buyers.

Question mark iconWhat is intelligent video analytics?
Intelligent video analytics is software that reads a video feed and turns what it sees into structured events: a vehicle recognized, a person counted, a zone entered, a process stopped. A model classifies the content, which is what the word intelligent marks off from simple motion detection. The output is an event stream an operations system can act on.
Question mark iconWhat is video analytics?
Video analytics is the broader practice of extracting measurements from camera feeds: counts, dwell times, classifications, and detections that fire an alert. It runs either on a server receiving streams or on the device itself. Where it runs decides most of the architecture, since sending video costs bandwidth and creates privacy exposure that on-device processing avoids.
Question mark iconHow does video analytics work?
Frames are captured, pre-processed and passed through a model trained for the task: detection, classification, tracking or recognition. The model returns a result with a confidence score, and rules turn results into events worth telling someone about. On Aeye Systems, recognition runs on hardware inside the vehicle so the decision happens where coverage ends, and events queue locally and replay on reconnect.
Question mark iconHow can video analytics help with loss prevention?
By turning cameras from an after-the-fact record into a live signal: counts at an entrance compared against transactions, detection of a known pattern at a self-checkout, or an alert when a restricted zone is entered outside hours. The value depends on that alert reaching somebody who can act inside the window where acting is still possible, which makes it a delivery problem as much as a detection one.
Question mark iconWhy is video analytics important?
Footage nobody watches carries little operational value. A site with a hundred cameras produces more video per day than any team can review, so the recording becomes evidence for incidents already lost. Analytics converts that into events at the moment they happen. On Numyas the same principle applies to still images, where a site photograph produces a construction progress score the same day.
Question mark iconWhy does recognition need to run on the device?
Because the connection cannot be relied on where the work happens. Aeye Systems runs models on NVIDIA Jetson hardware inside vehicles and fixed installations, so detections and watchlist matching keep working through an outage. Cloud-only inference stops recognising the moment the link drops.
Question mark iconCan these systems work with hardware we already have?
Usually. The audit lists the cameras and compute already deployed, checks resolution, placement and frame rate against what has to be recognised, and adds hardware only where the existing device cannot carry the model.
Question mark iconHow is evidence handled so it holds up later?
Every detection is recorded with its timestamp, the model version that produced it and the source frames behind it, in an evidence review and export layer built for agency scrutiny. That record is what makes an answer survive being questioned.
Question mark iconWhat accuracy can we expect?
It depends on the footage, the conditions and how the classes are defined, so the honest answer comes from a measured baseline. Hygge labels footage you already hold, trains against it and reports accuracy on a held-out set before anything is quoted as production-ready.
Question mark iconWhy does model accuracy drop after launch?
Because the field looks nothing like the training set. Lighting, weather, camera angles and the mix of what appears all shift. Drift monitoring measures live performance against the baseline and flags degradation before an operator reports it.
Question mark iconHow much does a video analytics platform cost?
The build is fixed after the audit. Running cost is edge hardware, the bandwidth used for results and clips, and the storage retention you choose, produced at your site and camera count before development starts.
Question mark iconHow long does a first phase take?
Two weeks for the audit, then a measured accuracy baseline before deployment, with a pilot site running edge inference and alerting inside three months.

Start With What the Camera Has to Catch

Tell us what needs recognising, where the cameras are and what the network does there. You get an audit, a hardware plan, a scope and an exact price.

Tell Us What Has to Be Recognised

Tell Us What Has to Be Recognised

Share the sites, the cameras already installed, what the system has to catch and what happens when it does.

Get a Site and Hardware Audit

Get a Site and Hardware Audit

Hygge checks compute, placement and connectivity at each site, and labels footage you already hold to set an accuracy baseline.

Receive a Hardware Plan and an Exact Price

Receive a Hardware Plan and an Exact Price

A written scope with the model approach, where inference runs, the evidence layer, the timeline and the cost.

Price this build

Tell us what the platform has to handle. You get the scope and the price before development starts.