Real-time mmWave Multi-Person Pose Estimation System for Privacy-Aware Windows

Abstract

Modern building facades and indoor partition walls feature large amounts of transparency for sufficient lighting and social safety. However, this transparency leads to concerns about privacy invasion, as sensitive objects, such as computer monitors, are exposed to onlookers. The advent of advanced screen technology has introduced VideowindoW, a smart installation capable of adjusting the transparency of its pixels to create a self-fading window, potentially addressing these privacy concerns. This thesis investigates the combined use of these smart windows together with mmWave radar technology, as a non-intrusive method to perform human posture estimation among multiple people. It develops a real-time system that detects passersby, localizes their head/eyes and obstructs their line-of-sight to sensitive indoor content, by projecting opaque squares on the smart screen. A notable gap in the mmWave literature is the insufficient handling of posture estimation challenges posed by multiple and dynamically moving targets. To address this gap, we propose the first, to our knowledge, mmWave-based Multi-Person Pose Estimation (MPPE) system. This system combines and improves two state-of-the-art methods for tracking and posture estimation and introduces a novel dataset for dynamic targets, including ground truth data for 19 human joints. Our solution demonstrated a 20% improvement in joint localization Mean Average Error (MAE) over the baseline system, in offline experiments with a single dynamic target. Furthermore, it achieved a mean blocking accuracy of 92% in online evaluations involving multiple people and varying environment. These results highlight a promising application in privacy shielding and lay the groundwork for further research in mmWave posture estimation in more unconstrained scenarios.