Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision

The widespread use of visual surveillance in public areas puts individual privacy at stake while also increasing resource usage (energy, bandwidth, and computation). Neuromorphic vision sensors (or event cameras) are considered viable solutions for privacy issues; since event cameras only capture scene dynamics, they do not capture detailed RGB images of individuals. However, recent deep learning architectures have enabled the reconstruction of high-fidelity images from event sensor data that could reveal individual identity information. As a result, it reintroduces privacy risks for event-based vision applications. In this work, we focus on protecting the identity of individuals from such image reconstruction attacks by anonymizing event data. To achieve this, we present an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream computer vision task. The proposed network learns to scramble events, thereby degrading the quality of images that potential intruders could reconstruct. We demonstrate the application of our framework in two challenging computer vision tasks: person re-identification (ReId) and human pose estimation (HPE). To this end, we also present and evaluate the first event-based person ReId dataset, Event-ReId. We validate the privacy-preserving efficacy of our approach against possible privacy attacks through extensive experiments: for person ReId, we utilize the real event-based Event-ReId dataset and synthetic event data simulated from the SoftBio dataset; for HPE, we use a publicly available event-based dataset DHP19. The results of both tasks show that anonymizing event data effectively protects private information with minimal impact on the subsequent task performance.