Virtual machines count people, vehicles and bicycles

We are pleased to announce that our bicycle, vehicle and people counting systems are now available as Virtual Machines. This means that instead of using our VT counting hardware, customers can instead simply run our new Virtual Machine application on their host computer or server.

  • No physical installation
  • No Cabling
  • No additional cameras

Virtual Machines can be deployed almost anywhere, regardless of the operating system or configuration of its host. They behave like an actual “machine”, or, in our case, as our Video Turnstile counting hardware.

Normally our systems work like this:

  • A camera connects to our Video Turnstile (VT) counting boxes
  • Software running within the VT boxes analyses the video feed from the camera and keeps count of people crossing the counting zone

In the new system, we’ve developed the software to run as a Virtual Machine, analysing a video feed and counting people: no counting hardware.

Virtual machine computing is becoming more and more widespread because of the convenience of cutting down the need for physical hardware and making systems independent.

The Virtual VT makes the counts available in real-time, to our reporting dashboards or to clients’ own systems. It can be part of an IoT system and runs, for example, in VMware, Virtual Box and Hyper V.

Vehicle Counting using Virtual Machines in the Yorkdale Shopping Mall

Spanning 2 million square feet and featuring 270 stores, Yorkdale is a premier retail destination in Canada. The Toronto shopping centre is now using virtual machines from Retail Sensing to monitor large goods vehicles into their loading area.

Recording numbers of heavy goods vehicles at the Yorkdale Shopping Mall in Toronto

Contact us for more details

David Collins

With over 12 years experience of video analytics and computer vision, David Collins' expertise covers AI-powered people and vehicle counting systems & software, and their associated applications. His specialities include neural networks and machine learning.

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