Crowd quantification with flow direction estimation : a low-cost IoT-enabled solution - Santil, Ricardo (2022)
Monitoring crowds in public environments is of great value for understanding human routines and managing crowd routes in indoor or outdoor environments. This type of information is crucial to improve the business strategy of an organization, and can be achieved by performing crowd quantification and flow direction estimation to generate information that can be later used by a business intelligence/analytic layer to improve sales of a specific service or targeting a new specific product. In this paper, we propose the design of an IoT Crowd sensor composed of an array of ultrasonic ping sensors that is responsible for detecting movement in specific directions. The proposed device has a built-in algorithm that is optimized to quantify and detect the human flow direction in indoor spaces such as hallways. Results have shown an average accuracy above 86% in the five scenarios evaluated when using an array with three elements.