A groundbreaking
technology platform.

Our unique cloud infrastructure.

  • A distributed platform built for mass scale.
  • Runs multiple computer vision algorithms from different video sources, all in one platform at the same time.
  • Tracks processing demands in real time and scales server usage automatically.

Using off-the-shelf, inexpensive, smart cameras.

smart camera

Our algorithms anonymise personal data on the camera, allowing it to be transferred to the cloud for processing.

Camera management platform.

Camera management platform

Setup, configuration and management of cameras by the customer.

Computer vision platform.

Object detection.

Recognise the correct type of object in view.
eg: Person, airplane or lorry.

Detecting people
Detecting airplanes
Detecting lorries


Follow the path of each detected object across the camera view in real-time.

Tracking objects

Event detection.

Identify the object performing a key event such as crossing a count line, taking off or stopping

People counting
Plane taking off
Lorries driving

Distributed state store

  • Event metadata cache shared across server network in real time. Platform autoscales based on demand.
Data caching Distributed cloud platform Distributed cache
Server cache

Event detection can trigger any kind of output.

This technology is used to tell (for instance):

  • There are 630 people in the building right now.
  • 48 people have exited the building in the last hour.
  • 221 people have walked past the building today.

Event notifications via SMS and email

Email, SMS, WhatsApp etc ...

Access our data via API

API trigger of another system.

Reporting, charts and data comparison

Charting / trends and data comparison.

It's a revolutionary new approach.

Our platform is radically different from existing solutions.

  HoxtonAi. Legacy systems.
Using low cost off the shelf cameras done close
Ultra-scalable cloud distributed system done close
Multiple algorithms on one platform done close
Real time aggregation of multiple camera streams done close
Self install for rapid up/down scaling done close
Use on any object (not just people) done close

Existing people counting systems use 3D sensors. These devices have become established by being accurate in real world situations which are likely to introduce varied lighting conditions, installation heights & angles and environmental factors such as reflective floors.

Processing of video from these sensors is performed on the device as a linear stream. Real-time aggregation of people count data is not possible, limiting its application of use. While the sensing algorithms are highly accurate, each sensor is limited to running just one algorithm, again limiting the diversity of application.

They're also limited to only counting people.

A technical revolution

Our algorithms anonymise personal data on the camera, allowing it to be transferred to the cloud for processing. They have been trained to accurately count pedestrians, giving customers a self-configurable cloud based computer vision system. For the purposes of footfall and occupancy analytics our system is as accurate as competitors technology - with the advantage of being lower cost and more flexible.

Importantly, our technology is in no way limited to just people counting. The underlying infrastructure of real-time multi-threaded cloud processing, metadata caching, multiple algorithms and rapid scalability can be applied to myriad other physical events. The algorithms are designed to be re-trained to count airplane landings, boxes on a conveyor line, hard hats on a building site or whatever type of visible 'thing' a business might want to report on.

How we've done it

The limitations of existing tehnologies:

  • Hardware costs.
  • Expert installations.
  • High energy consumption.

Our technology deliveres the same counting accuracy with the additional benefits of:

  • Can be trained to count almost any type of physical object.
  • Using off-the-shelf (inexpensive) cameras.
  • Self installation by customers.
  • Efficient resource allocation with distributed cloud processing.
  • Real time data reporting across multiple camera streams.
The benefits of our technology

We are building:

  • A distributed system.
  • That has high detection accuracy.
  • With anonymised video.
  • Maintaining per-video metadata.
  • Processing video chunks in real time.
  • That works at scale.
Difficult solutions

Allowing the use of simple, off the shelf cameras offers huge potential benefits over existing state-of-the-art technologies with dedicated cameras. Cost reductions and ease of install makes takeup from customers much lower risk. However, using less 'powerful' cameras means a very limited amount of processing can be done on the device. The simple solution is to upload and process each camera stream individually but this has a number of limitations and in particular is an inefficient utilisation of server resources (less than 30% efficiency).

The challenge in distributing video for processing is:

  • Maintaining a shared cache across all cameras and videos.
  • Be robust against delay, gaps and detection inaccuracy in the video.
Distributed platform