  In this paper, we present a room occupancy sensing solution with unique
properties: (i) It is based on an omnidirectional vision camera, capturing rich
scene info over a wide angle, enabling to count the number of people in a room
and even their position. (ii) Although it uses a camera-input, no privacy
issues arise because its extremely low image resolution, rendering people
unrecognisable. (iii) The neural network inference is running entirely on a
low-cost processing platform embedded in the sensor, reducing the privacy risk
even further. (iv) Limited manual data annotation is needed, because of the
self-training scheme we propose. Such a smart room occupancy rate sensor can be
used in e.g. meeting rooms and flex-desks. Indeed, by encouraging flex-desking,
the required office space can be reduced significantly. In some cases, however,
a flex-desk that has been reserved remains unoccupied without an update in the
reservation system. A similar problem occurs with meeting rooms, which are
often under-occupied. By optimising the occupancy rate a huge reduction in
costs can be achieved. Therefore, in this paper, we develop such system which
determines the number of people present in office flex-desks and meeting rooms.
Using an omnidirectional camera mounted in the ceiling, combined with a person
detector, the company can intelligently update the reservation system based on
the measured occupancy. Next to the optimisation and embedded implementation of
such a self-training omnidirectional people detection algorithm, in this work
we propose a novel approach that combines spatial and temporal image data,
improving performance of our system on extreme low-resolution images.
