Person De-identification in Videos

  • Prachi Agrawal
  • P. J. Narayanan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)


Advances in cameras and web technology have made it easy to capture and share large amounts of video data over to a large number of people through services like Google Street View, EveryScape, etc. A large number of cameras oversee public and semi-public spaces today. These raise concerns on the unintentional and unwarranted invasion of the privacy of individuals caught in the videos. To address these concerns, automated methods to de-identify individuals in these videos are necessary. De-identification does not aim at destroying all information involving the individuals. Its goals are to obscure the identity of the actor without obscuring the action. This paper outlines the scenarios in which de-identification is required and the issues brought out by those. We also present a preliminary approach to de-identify individuals from videos. A bounding box around each individual present in a video is tracked through the video. An outline of the individuals is approximated by carrying out segmentation on a 3-D Graph of space-time voxels. We explore two de-identification transformations: exponential space-time blur and line integral convolution. We show results on a number of public videos and videos collected in a plausible setting. We also present the preliminary results of a user-study to validate the effectiveness of the de-identification schemes.


User Study Privacy Protection Foreground Pixel Smoothness Term Computer Vision Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Prachi Agrawal
    • 1
  • P. J. Narayanan
    • 1
  1. 1.Center for Visual Information TechnologyIIITHyderabadIndia

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