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PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

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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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Author information

Authors and Affiliations

  1. Universidad Industrial de Santander, Bucaramanga, Colombia

    Carlos Hinojosa, Miguel Marquez & Henry Arguello

  2. Stanford University, Stanford, USA

    Carlos Hinojosa, Ehsan Adeli, Li Fei-Fei & Juan Carlos Niebles

Authors
  1. Carlos Hinojosa
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  2. Miguel Marquez
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  3. Henry Arguello
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  4. Ehsan Adeli
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  5. Li Fei-Fei
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  6. Juan Carlos Niebles
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Corresponding author

Correspondence to Carlos Hinojosa .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Hinojosa, C., Marquez, M., Arguello, H., Adeli, E., Fei-Fei, L., Niebles, J.C. (2022). PrivHAR: Recognizing Human Actions from Privacy-Preserving Lens. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_19

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  • DOI: https://doi.org/10.1007/978-3-031-19772-7_19

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