Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Nawaz, Tahir; Ferryman, James (2015)
Languages: English
Types: Unknown
While several privacy protection techniques are pre- sented in the literature, they are not complemented with an established objective evaluation method for their assess- ment and comparison. This paper proposes an annotation- free evaluation method that assesses the two key aspects of privacy protection that are privacy and utility. Unlike some existing methods, the proposed method does not rely on the use of subjective judgements and does not assume a spe- cific target type in the image data. The privacy aspect is quantified as an appearance similarity and the utility aspect is measured as a structural similarity between the original raw image data and the privacy-protected image data. We performed an extensive experimentation using six challeng- ing datasets (including two new ones) to demonstrate the effectiveness of the evaluation method by providing a per- formance comparison of four state-of-the-art privacy pro- tection techniques.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] ETH Bahnhof and Sunnyday http://www.vision.ee.ethz.ch/~aess/iccv2007/. March 2015.
    • Accessed 0.5 00 0.5 1 Privacy (P) 0.5 1 Privacy (P) 2 1.5 2 1.5 2
    • [2] EU project P5. http://www.foi.se/p5. Accessed June 2015.
    • [3] http://www.eecs.qmul.ac.uk/~andrea/avss2007 d.html. Accessed March 2015.
    • [4] PETS 2000 dataset. ftp://ftp.cs.rdg.ac.uk/pub/PETS2000/. Accessed March 2015.
    • [5] A. J. Aved and K. A. Hua. A general framework for managing and processing live video data with privacy protection. Multimedia systems, 18(2):123-143, 2012.
    • [6] S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black, and R. Szeliski. A database and evaluation methodology for optical flow. IJCV, 92(1):1-31, 2011.
    • [7] M. Boyle, C. Edwards, and S. Greenberg. The effects of filtered video on awareness and privacy. In Proc. of CSCW, 2000.
    • [8] A. Cavallaro. Privacy in video surveillance. IEEE SPM, 24(2):165-166, 2007.
    • [9] F. Defaux. Video scrambling for privacy protection in video surveillance: recent results and validation framework. In Proc. of SPIE, 2011.
    • [10] A. Erdelyi, T. Barat, P. Valet, T. Winkler, and B. Rinner. Adaptive cartooning for privacy protection in camera networks. In Proc. of IEEE AVSS, 2014.
    • [11] S. Fleck and W. Strasser. Smart camera based monitoring system and its application to assisted living. Proceedings of IEEE, 96(10):1698-1714, 2008.
    • [12] B.-J. Han, H. Jeong, and Y.-J. Won. The privacy protection framework for biometric information in network based cctv environment. In Proc. of ICOS, 2011.
    • [13] P. Korshunov, C. Araimo, F. D. Simone, C. Velardo, J.-L. Dugelay, and T. Ebrahimi. Subjective study of privacy filters in video surveillance. In Proc. of IEEE Work. MMSP, 2012.
    • [14] P. Korshunov, A. Melle, J.-L. Dugelay, and T. Ebrahimi. Framework for objective evaluation of privacy filters. In Proc. of SPIE, 2013.
    • [15] M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, F. Porikli, L. Cehovin, G. Nebehay, G. Fernandez, and T. Vojir. The vot2013 challenge: overview and additional results. In Proc. of CVWW, 2014.
    • [16] A. Martinez-Balleste, H. A. Rashwan, D. Puig, and A. P. Fullana. Towards a trustworthy privacy in pervasive video surveillance systems. In Proc. of IEEE PerCom, 2012.
    • [17] T. Nawaz, F. Poiesi, and A. Cavallaro. Measures of effective video tracking. IEEE TIP, 23(1):376-388, 2014.
    • [18] J. Ning, L. Zhang, D. Zhang, and C. Wu. Robust mean-shift tracking with corrected background-weighted histogram. IET Comp. Vis., 6(1):62-69, 2012.
    • [19] H. Pirsiavash, D. Ramanan, and C. C. Fowlkes. Globallyoptimal greedy algorithms for tracking a variable number of objects. In Proc. of IEEE CVPR, 2011.
    • [20] F. Z. Qureshi. Object-video streams for preserving privacy in video surveillance. In IEEE AVSS, 2009.
    • [21] M. Saini, P. Atrey, S. Mehrotra, and M. Kankanhalli. Anonymous surveillance. In Proc. of IEEE ICME, 2011.
    • [22] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1/2/3):7-42, 2002.
    • [23] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004.
    • [24] B. L. Welch. On the comparison of several mean values: An alternative approach. Biomet., 38(3-4):330-336, 1951.
    • [25] T. Winkler and B. Rinner. Security and privacy protection in visual sensor networks: A survey. ACM Comp. Surv., 47(1), 2014.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Funded by projects

  • EC | P5

Cite this article