  Camera-based activity monitoring systems are becoming an attractive solution
for smart building applications with the advances in computer vision and edge
computing technologies. In this paper, we present a feasibility study and
systematic analysis of a camera-based indoor localization and multi-person
tracking system implemented on edge computing devices within a large indoor
space. To this end, we deployed an end-to-end edge computing pipeline that
utilizes multiple cameras to achieve localization, body orientation estimation
and tracking of multiple individuals within a large therapeutic space spanning
$1700m^2$, all while maintaining a strong focus on preserving privacy. Our
pipeline consists of 39 edge computing camera systems equipped with Tensor
Processing Units (TPUs) placed in the indoor space's ceiling. To ensure the
privacy of individuals, a real-time multi-person pose estimation algorithm runs
on the TPU of the computing camera system. This algorithm extracts poses and
bounding boxes, which are utilized for indoor localization, body orientation
estimation, and multi-person tracking. Our pipeline demonstrated an average
localization error of 1.41 meters, a multiple-object tracking accuracy score of
88.6\%, and a mean absolute body orientation error of 29\degree. These results
shows that localization and tracking of individuals in a large indoor space is
feasible even with the privacy constrains.
