A Benchmarking Framework for Privacy-Preserving Human Pose Estimation on Edge Devices

Deploying human pose estimation (HPE) pipelines in privacy-aware settings requires processing sensitive data close to the point of acquisition, as set out in regulations such as the AI Act. Edge devices can meet this requirement, but their limited resources modify the impact of privacy mechanisms, hardware capacity, and scene complexity on latency. This paper presents a framework that jointly analyzes latency and privacy in HPE pipelines executed on edge architectures. We consider two representative scenarios: i) all the pipeline stages on the acquisition device; ii) acquisition and obfuscation on the camera, with inference on a separate edge node. We present the framework by evaluating each scenario with deployments based on three state-of-the-art HPE models (MoveNet, YOLO-Pose, and OpenPose), three representative hardware configurations, and three levels of scene complexity based on the number of subjects and visual entropy. For each deployment, the framework estimates stage-level and end-to-end latency distributions, obtains the probability of completing the pipeline within task-specific deadlines, and the attainable privacy level. Our framework also generates reliability-privacy-cost maps, a decision support tool that relates improvements in timing and privacy to the cost of implementing or upgrading a deployment. A surveillance case study illustrates how the framework helps design or adapt privacy-preserving edge infrastructures.