PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens

Carlos Hinojosa, Miguel Marquez, Henry Arguello, Ehsan Adeli, Li Fei-Fei, Juan Carlos Niebles

ECCV 2022

doi:10.1007/978-3-031-19772-7_19 /eccv/2022/hinojosa2022eccv-privhar/

Abstract

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.

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Text

Hinojosa et al. "PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19772-7_19

Markdown

[Hinojosa et al. "PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/hinojosa2022eccv-privhar/) doi:10.1007/978-3-031-19772-7_19

BibTeX

@inproceedings{hinojosa2022eccv-privhar,
  title     = {{PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens}},
  author    = {Hinojosa, Carlos and Marquez, Miguel and Arguello, Henry and Adeli, Ehsan and Fei-Fei, Li and Niebles, Juan Carlos},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2022},
  doi       = {10.1007/978-3-031-19772-7_19},
  url       = {https://mlanthology.org/eccv/2022/hinojosa2022eccv-privhar/}
}