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Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns

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Protecting Privacy in Video Surveillance

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Abstract

To address privacy concerns regarding digital video surveillance cameras, we propose a practical, real-time approach that preserves the ability to observe actions while obscuring individual identities. In the Respectful Cameras system, people who wish to remain anonymous wear colored markers such as hats or vests. The system automatically tracks these markers using statistical learning and classification to infer the location and size of each face. It obscures faces with solid ellipsoidal overlays, while minimizing the overlay area to maximize the remaining observable region of the scene. Our approach uses a visual color-tracker based on a 9D color-space using a Probabilistic Adaptive Boosting (AdaBoost) classifier with axis-aligned hyperplanes as weak hypotheses. We then use Sampling Importance Resampling (SIR) Particle Filtering to incorporate interframe temporal information. Because our system explicitly tracks markers, our system is well-suited for applications with dynamic backgrounds or where the camera can move (e.g., under remote control). We present experiments illustrating the performance of our system in both indoor and outdoor settings, with occlusions, multiple crossing targets, lighting changes, and observation by a moving robotic camera. Results suggest that our implementation can track markers and keep false negative rates below 2%.

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Author information

Authors and Affiliations

  1. Department of EECS, University of California, Berkeley, CA, USA

    Jeremy Schiff & Marci Meingast

  2. Faculty of the School of Information, University of California, Berkeley, CA, USA

    Deirdre K. Mulligan

  3. Faculty of the Department of EECS, University of California, Berkeley, CA, USA

    Shankar Sastry

  4. Faculty of Departments of EECS and IEOR, University of California, Berkeley, CA, USA

    Ken Goldberg

Authors
  1. Jeremy Schiff
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  2. Marci Meingast
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  3. Deirdre K. Mulligan
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  4. Shankar Sastry
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  5. Ken Goldberg
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Corresponding author

Correspondence to Jeremy Schiff .

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Editors and Affiliations

  1. Google Research, New York, USA

    Andrew Senior

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Schiff, J., Meingast, M., Mulligan, D.K., Sastry, S., Goldberg, K. (2009). Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns. In: Senior, A. (eds) Protecting Privacy in Video Surveillance. Springer, London. https://doi.org/10.1007/978-1-84882-301-3_5

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  • DOI: https://doi.org/10.1007/978-1-84882-301-3_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-300-6

  • Online ISBN: 978-1-84882-301-3

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