Positioning recognition and people counting solutions play an important role in indoor contexts where it is necessary to monitor the number of people entering an industrial environment.
Classical approaches to positioning recognition and people counting tasks employ camera sensors, which despite often leading to accurate assessments, have considerable disadvantages such as privacy and latency. These problems are even more significant if Cloud-based systems are employed, due to data storage needs and the consequent introduction of communication latency. To solve these issues, the goal of the UC 1.1 is to develop an efficient, cost effective and privacy friendly application based on an edge computing solution using a neuromorphic chip inside the sensor to count and track people in an indoor space. This use case will explore potential approaches in this context by using radar sensors and thermopile array sensors with SNN hardware.
IFAG, in collaboration with EESY and TUD, works on both indoor positioning recognition and people counting, using a 60 GHz radar sensor and developed SNN chips.
