Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images

Steven Puttemans, Stef Van Wolputte, Toon Goedemé

Abstract

When capturing images in the wild containing pedestrians, privacy issues remain a major concern for industrial applications. Our application, collecting cycloramic mobile mapping data in crowded environments, is an example of this. If the data is processed and accessed by third parties, privacy of pedestrians must be ensured. This is where pedestrian detectors come into play, used to detect individuals and privacy mask them through blurring. The problem of undesired false positive detections, typical for pedestrian detectors and unavoidable, still leaves undesired areas of the images being blurred. We tackled this problem using application-specific scene constraints, modelled by a height-position mapping based on scene-specific pedestrian annotation data, combined with reducing the field of interest and case-specific false positive elimination classifiers. We applied a soft blurring technique to avoid the artificial look of simply applying Gaussian blurring to the found detections, which results in an effective fully-automated masking pipeline for privacy safeguarding in mobile mapping images. We prove that we can use pre-trained pedestrian detection models, but by collecting a limited amount of application-specific annotations and by exploiting scene-specific constraints, we are able to boost the detection accuracy enormously.

References

  1. Cho, H., Rybski, P. E., Bar-Hillel, A., and Zhang, W. (2012). Real-time pedestrian detection with deformable part models. In IVS, pages 1035-1042. IEEE.
  2. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In CVPR, volume 1, pages 886-893. IEEE.
  3. De Smedt, F., Struyf, L., Beckers, S., Vennekens, J., De Samblanx, G., and Goedemé, T. (2012). Is the game worth the candle? Evaluation of OpenCL for object detection algorithm optimization. PECCS, pages 284-291.
  4. Dibra, E., Maye, J., Diamanti, O., Siegwart, R., and Beardsley, P. (2015). Extending the performance of human classifiers using a viewpoint specific approach. In WACV, pages 765-772. IEEE.
  5. Dollár, P., Belongie, S., and Perona, P. (2010). The fastest pedestrian detector in the west. In BMVC, volume 2, page 7. Citeseer.
  6. Dollár, P., Tu, Z., Perona, P., and Belongie, S. (2009). Integral channel features. In BMVC, volume 2, page 5.
  7. Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In CVPR, pages 1-8. IEEE.
  8. Nakashima, Y., Koyama, T., Yokoya, N., and Babaguchi, N. (2015). Facial expression preserving privacy protection using image melding. In ICME, pages 1-6. IEEE.
  9. Panagiotis, I. (2015). Preventing privacy leakage from photos in social networks. In CCS2015. ACM.
  10. Peng, P., Tian, Y., Wang, Y., Li, J., and Huang, T. (2015). Robust multiple cameras pedestrian detection with multi-view bayesian network. Pattern Recognition, 48(5):1760-1772.
  11. Puttemans, S. and Goedemé, T. (2013). How to exploit scene constraints to improve object categorization algorithms for industrial applications. In VISAPP, volume 1, pages 827-830.
  12. Tanaka, Y., Kodate, A., Ichifuji, Y., and Sonehara, N. (2015). Relationship between willingness to share photos and preferred level of photo blurring for privacy protection. In ASE BigData & SocialInformatics, page 33. ACM.
  13. Torralba, A., Efros, A., et al. (2011). Unbiased look at dataset bias. In CVPR, pages 1521-1528. IEEE.
  14. Van Beeck, K., Goedemé, T., and Tuytelaars, T. (2012). A warping window approach to real-time vision-based pedestrian detection in a truck's blind spot zone. In ICINCO, volume 2, pages 561-568.
  15. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In CVPR, pages I-511.
Download


Paper Citation


in Harvard Style

Puttemans S., Van Wolputte S. and Goedemé T. (2016). Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 406-415. DOI: 10.5220/0005784304060415


in Bibtex Style

@conference{visapp16,
author={Steven Puttemans and Stef Van Wolputte and Toon Goedemé},
title={Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={406-415},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005784304060415},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images
SN - 978-989-758-175-5
AU - Puttemans S.
AU - Van Wolputte S.
AU - Goedemé T.
PY - 2016
SP - 406
EP - 415
DO - 10.5220/0005784304060415