A groundbreaking
technology platform.

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.

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.

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.

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.

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.