GDPR-Compliant Person Recognition in Industrial Environments Using MEMS-LiDAR and Hybrid Data

2026-02-02

AI Summary

This work proposes an anonymized 3D point cloud–based person detection method using MEMS-LiDAR, addressing the limitations of conventional vision-based approaches in industrial indoor environments—namely, their susceptibility to lighting and visibility conditions and their difficulty in complying with privacy regulations such as GDPR. The approach introduces a hybrid training dataset that uniquely combines real-world MEMS-LiDAR data with synthetic LiDAR data generated from the CARLA simulator, enabling effective training of deep learning–based object detection models. By leveraging this synthetic data augmentation strategy, the method not only ensures privacy compliance but also reduces manual annotation costs by 50% and improves mean average precision by 44 percentage points compared to models trained solely on real data, thereby demonstrating the efficacy and practicality of synthetic data in industrial applications.

Technology Category

• Machine Learning: Privacy
• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The reliable detection of unauthorized individuals in safety-critical industrial indoor spaces is crucial to avoid plant shutdowns, property damage, and personal hazards. Conventional vision-based methods that use deep-learning approaches for person recognition provide image information but are sensitive to lighting and visibility conditions and often violate privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union. Typically, detection systems based on deep learning require annotated data for training. Collecting and annotating such data, however, is highly time-consuming and due to manual treatments not necessarily error free. Therefore, this paper presents a privacy-compliant approach based on Micro-Electro-Mechanical Systems LiDAR (MEMS-LiDAR), which exclusively captures anonymized 3D point clouds and avoids personal identification features. To compensate for the large amount of time required to record real LiDAR data and for post-processing and annotation, real recordings are augmented with synthetically generated scenes from the CARLA simulation framework. The results demonstrate that the hybrid data improves the average precision by 44 percentage points compared to a model trained exclusively with real data while reducing the manual annotation effort by 50 %. Thus, the proposed approach provides a scalable, cost-efficient alternative to purely real-data-based methods and systematically shows how synthetic LiDAR data can combine high performance in person detection with GDPR compliance in an industrial environment.

Problem

• GDPR compliance
• person recognition
• industrial environments
• privacy-preserving sensing
• LiDAR-based detection

Innovation

• MEMS-LiDAR
• GDPR-compliant
• hybrid data
• synthetic LiDAR
• person recognition