PERSON RE-IDENTIFICATION WITHOUT IDENTIFICATION VIA EVENT ANONYMIZATION
ISTITUTO ITALINO DI TECNOLOGIA, UNIVERSITA DEGLI STUDI DI GENOVA
Shafiq Ahmad, Pietro Morerio, Alessio Del Bue
ABSTRACT
As Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks.
