Team for Research in
Ubiquitous Secure Technology

Citation
Jeremy Schiff, Marci Meingast, Deirdre Mulligan, Shankar Sastry, Ken Goldberg. "Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns". Protecting Privacy in Video Surveillance, Andrew W. Senior, Springer., June 2009.

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 nine dimensional 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%.

Electronic downloads

• respectful-cameras-book-chapter-F08.pdf · application/pdf · 1760 kbytes

• HTML

  Jeremy Schiff, Marci Meingast, Deirdre Mulligan, Shankar
  Sastry, Ken Goldberg. <a
  href="http://www.truststc.org/pubs/702.html"
  >Respectful Cameras: Detecting Visual Markers in
  Real-Time to Address Privacy Concerns</a>,
  <i>Protecting Privacy in Video Surveillance, Andrew W.
  Senior, Springer.</i>, June 2009.

• Plain text

  Jeremy Schiff, Marci Meingast, Deirdre Mulligan, Shankar
  Sastry, Ken Goldberg. "Respectful Cameras: Detecting
  Visual Markers in Real-Time to Address Privacy
  Concerns". <i>Protecting Privacy in Video
  Surveillance, Andrew W. Senior, Springer.</i>, June
  2009.

• BibTeX

  @article{SchiffMeingastMulliganSastryGoldberg09_RespectfulCamerasDetectingVisualMarkersInRealTimeTo,
      author = {Jeremy Schiff and Marci Meingast and Deirdre
                Mulligan and Shankar Sastry and Ken Goldberg},
      title = {Respectful Cameras: Detecting Visual Markers in
                Real-Time to Address Privacy Concerns},
      journal = {Protecting Privacy in Video Surveillance, Andrew
                W. Senior, Springer.},
      month = {June},
      year = {2009},
      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
                nine dimensional 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%.},
      URL = {http://www.truststc.org/pubs/702.html}
  }
  

Posted by Jessica Gamble on 5 Apr 2010.
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