Track multiple people and their gaze direction using many edge TPU and pose for privacy with Kwon
Track multiple people and their gaze direction using many edge TPU and pose for privacy with Kwon
Indoor Localization and Multi-person Tracking Using Privacy Preserving Distributed Camera Network with Edge Computing
arXiv paper abstract https://arxiv.org/abs/2305.05062
arXiv PDF paper https://arxiv.org/pdf/2305.05062.pdf
... Estimating the positions, face orientation (or gaze direction) and trajectories of people through space has many uses, such as in crowd management, security, and healthcare.
... present an open-source, low-cost, scalable and privacy-preserving edge computing framework for multi-person localization, i.e. estimating the positions, orientations, and trajectories of multiple people in an indoor space.
... computing framework consists of 38 Tensor Processing Unit (TPU)-enabled edge computing camera systems placed in the ceiling of the indoor therapeutic space.
... A multi-person detection algorithm and a pose estimation model run on the edge TPU in real-time to collect features which are used, instead of raw images, for downstream computations.
... study site with size of 18,000 square feet, ... system demonstrated an average localization error of 1.41 meters, a multiple-object tracking accuracy score of 62%, and a mean absolute body orientation error of 29°, which is sufficient for understanding group activity behaviors in indoo
or environments.
... study provides practical guidance for deploying the proposed system by analyzing various elements of the camera installation with respect to tracking accuracy.
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