Occupancy tracking is becoming crucial in many people-centric applications, such as Social Distance regulatory compliance, Crowd Control, Building & Energy Management, Smart Cities and Transportation.

Crowd behaviours are usually unpredictable, which pose many challenges for crowd counting and estimation. Other challenges may include object occlusions and real-time processing requirement.

Classical solutions can be broadly categorized as “image-based” and “non-image-based” methods. An image-based counting model estimates the crowd density by analyzing the human characteristics in high-resolution images in the pixel, texture,or object level, often achieving a superb detection accuracy. However, sensitivity to scenario brightness, high computational costs, and privacy concerns are limiting factors that could confine the applicability of such image-based methods.

Bluewind presents here a “non-image-based” solution: an AI-based software platform designed to compute the number of people in indoor environment, providing real-time and accurate estimation of the crowd size.