Work

Indoor Group Identification and Localization Using Privacy-Preserving Edge Computing Distributed Camera Network

Year: 2024

Type: article

Abstract: Social interaction behaviors change as a result of both physical and psychiatric problems, and it is important to identify subtle changes in group activity engagements for monitoring the mental health of patients in clinics. This work proposes a system to identify when and where group formations occur in an approximately 1,700$m^{2}$therapeutic built environment using a distributed edge-computing camera network. The proposed method can localize group formations when provided with noisy positions and orientations of individuals, estimated from sparsely distributed multi-view cameras, which run a lightweight multi-person 2D pose detection model. Our group identification method demonstrated an F1 score of up to 90% with a mean absolute error of 1.25 m for group localization on our benchmark dataset. The dataset consisted of 7 subjects walking, sitting, and conversing for 35 minutes in groups of various sizes ranging from 2 to 7 subjects. The proposed system is low-cost and scalable to any ordinary building to transform the indoor space into a smart environment using edge computing systems. We expect the proposed system to enhance existing therapeutic units for passively monitoring the social behaviors of patients when implementing real-time interventions.

Source: IEEE Journal of Indoor and Seamless Positioning and Navigation

DOI: 10.1109/jispin.2024.3354248