Visionairy

Visionairy are dedicated Computer Vision experts. Through the use of Deep Learning algorithms they teach computers to see like human beings.

In the past two years they successfully delivered projects in sports, process industry, and retail. Their clients include:

360 Sports intelligence: market leader in the field of live sports streaming for sports that don’t receive enough attention to be on national television. They have been partners in the success story of this remarkable scale-up since the beginning.

360 installs camera’s alongside the playing field. It offers a streaming service which can be used for recreational and coaching purposes. They created an algorithm that is able to automatically steer the camera, based on a prediction of the ball's position, which is based on the movements of players (since the ball is too small in a sport like fieldhockey). Currently, their applications are used in football, fieldhockey, volleyball, futsal, rugby and waterpolo. The generated game data is used for realtime game registration, statistics and highlights to be used for entertainment and post-game analysis. 360 currently is the biggest player in the Netherlands, with almost national coverage of sports like fieldhockey, baseball, basketball and volleyball. As we speak they are rolling out the system we co-developed in Ivy league universities, such as Harvard.

Elis: one of the biggest international players for industrial laundry. We’ve developed and implemented an automated quality control solution that checks for erroneous entries into the distribution system. Accidentally mixing white- and coloured laundry caused major disruptions and high replacement costs at an industrial laundrette. Our application succesfully prevents this mix-up by classifying laundry bags before washing. The solution we made for them saves tens of thousands of euro’s per location, per year. Since our solution is live, no more laundry has been damaged irreparably.

Hema: one of the biggest retail chains in the Netherlands. For an R&D project they’ve used in-store cameras to extract anonymized customer data to gain insights like time-in-shop, conversion rates, customer-routing and peak hours. This data can be used to optimize store layout and staff planning. Moreover, they are experimenting with an algorithm for automatic stock detection in retail stores.