The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks while maintaining relevant features for activity recognition. We validate our approach with extensive simulations and hardware experiments.
@article{arxiv.2206.03891,
title = {PrivHAR: Recognizing Human Actions From Privacy-preserving Lens},
author = {Carlos Hinojosa and Miguel Marquez and Henry Arguello and Ehsan Adeli and Li Fei-Fei and Juan Carlos Niebles},
journal= {arXiv preprint arXiv:2206.03891},
year = {2023}
}
Comments
Oral paper presented at European Conference on Computer Vision (ECCV) 2022, in Tel Aviv, Israel