# A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing

arXiv: https://arxiv.org/abs/2305.05062 · HTML: https://mlsanity.com/paper/2305.05062/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [MEBOW: Monocular Estimation of Body Orientation In the Wild](https://mlsanity.com/paper/2011.13688/) (2020)
- [Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance](https://mlsanity.com/paper/1708.00163/) (2017)
- [Vision-Based Gait Analysis for Senior Care](https://mlsanity.com/paper/1812.00169/) (2018)

## Built on by (most cited first, 3 of 3)

- [Feasibility of assessing cognitive impairment via distributed camera network and privacy-preserving edge computing](https://mlsanity.com/paper/2408.10442/) (2024)
- [Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis](https://mlsanity.com/paper/2407.21691/) (2024)
- [Measurement-Calibrated Multi-Camera Fusion for Vision-Based Indoor Localization](https://mlsanity.com/paper/2606.13509/) (2026)

## Abstract (arXiv)

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 1700m2, 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 29degree. These results shows that localization and tracking of individuals in a large indoor space is feasible even with the privacy constrains.