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Established in 2002, FootfallCam is a privately held company and leading global provider of people counting solutions.
We have a team of highly skilled engineers, manufacturing both hardware and software fully in-house in our U.K Headquarter, and applying their expertise in chosen fields to solve business challenges over the years.
With a global network of reseller partners and continuous effort in research and development, we have relentlessly served organisations all over the world in sectors varying from retail, fast food restaurants, and museums to smart buildings and airports, for more than a decade.
Our company values and focus on our Research and Development (R&D) as part of our core strategy.
We have an in-house R&D team dedicated to the development of both hardware and software of FootfallCam.
Continuous development of the product allows FootfallCam to stay at the forefront in the people counting industry.
Coupled with our innovative approach to product development, we have successfully developed our flagship product, FootfallCam 3D Max.
We are the first in the world that combine people counting and Wi-Fi analytics into a single device.
We are committed to continually maintain our market leading position, bringing a great deal of strategic foresight that our customers require.
FootfallCam expands its presence globally by setting up sales offices in several countries such as in the United Kingdom, United States, Hong Kong and Malaysia.
Over the years, besides setting up sales offices, FootfallCam has developed a strong partner network with its presence at more than 100 countries such as Ireland, France, Italy, Spain, United Arab Emirates, South Africa, Thailand, Singapore, Indonesia, Germany, Australia and others.
We have over 100 years of combined industry experience in developing both hardware and software for people counting solutions.
Stereoscopic (3D image processing) being able to intelligently distinguish a human from non-human objects and track directional movement of a walking human.
Artificial Intelligence using a Support Vector Machine (SVM) neural network model to train our machines on how to recognize something.