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Advances in Multimedia
Volume 2012 (2012), Article ID 639649, 14 pages
Research Article

Adaptive Transformation for Robust Privacy Protection in Video Surveillance

1School of Computing, National University of Singapore, Singapore 117417
2Department of Applied Computer Science, The University of Winnipeg, MB, Canada R3T 5V9
3Information and Computer Science Department, University of California, Irvine, CA 92697-3425, USA

Received 30 November 2011; Revised 6 February 2012; Accepted 6 February 2012

Academic Editor: Martin Reisslein

Copyright © 2012 Mukesh Saini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Privacy is a big concern in current video surveillance systems. Due to privacy issues, many strategic places remain unmonitored leading to security threats. The main problem with existing privacy protection methods is that they assume availability of accurate region of interest (RoI) detectors that can detect and hide the privacy sensitive regions such as faces. However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. Further, there are many implicit privacy leakage channels that have not been considered by researchers for privacy protection. We block both implicit and explicit channels of privacy leakage. Experimental results show that the proposed method incurs 38% less distortion of the information needed for surveillance in comparison to earlier methods of global transformation; while still providing near-zero privacy loss.