Large-scale Privacy Protection in Google Street View
Venue
IEEE International Conference on Computer Vision (2009)
Publication Year
2009
Authors
Andrea Frome, German Cheung, Ahmad Abdulkader, Marco Zennaro, Bo Wu, Alessandro Bissacco, Hartwig Adam, Hartmut Neven, Luc Vincent
BibTeX
Abstract
The last two years have witnessed the introduction and rapid expansion of products
based upon large, systematically-gathered, street-level image collections, such as
Google Street View,
EveryScape, and Mapjack. In the process of gathering images of public
spaces, these projects also capture license plates, faces, and other information
considered sensitive from a privacy standpoint. In this work, we present a system
that addresses the challenge of automatically detecting and blurring faces and
license plates for the purpose of privacy protection in Google Street View. Though
some in the field would claim face detection is "solved", we show that
state-of-the-art face detectors alone are not sufficient to achieve the recall
desired for large-scale privacy protection. In this paper we present a system that
combines a standard sliding-window detector tuned for a high recall, low-precision
operating point with a fast post-processing stage that is able to remove additional
false positives by incorporating domain-specific information not available to the
sliding-window detector. Using a completely automatic system, we are able to
sufficiently blur more than 89% of faces and 94-96% of license plates in evaluation
sets sampled from Google Street View imagery.
The full paper will appear from IEEE.
