Multimedia Systems

, Volume 18, Issue 2, pp 95–97 | Cite as

Guest editorial: Privacy-aware multimedia surveillance systems

  • Pradeep K. Atrey
  • Sabu Emmanuel
  • Sharad Mehrotra
  • Mohan S. Kankanhalli

Due to the significant increase in various security threats, safety has become a primary concern for our society. As a result, most of the public places such as airports, train stations, banks, shopping malls, subways and streets are nowadays equipped with state-of-the-art multimedia surveillance systems. These systems are meant to process sensory data to automatically detect suspicious or unusual behavior of people and alert security personnel so that preventive actions can be taken. Although such surveillance infrastructure has proved to be very useful from a security perspective; there has been apprehension among people in regard to their privacy safeguards. Citizens have increasingly begun to object to being watched electronically. Hence, there is a need for preserving privacy of people yet providing them a sense of security through effective surveillance. It is worth mentioning that although there has been a significant progress in the field of surveillance research, the issues related to people’s privacy have often been overlooked in the past and have only begun to attract the attention of researchers very recently. The goal of this special issue is to bring forth the recent advances in the privacy research for multimedia surveillance.

We received nine submissions from an open call for papers that address different aspects of privacy-aware multimedia surveillance systems. Although many submissions were of good quality, guest editorial committee recommended to accept only five top quality papers after a careful and highly competitive review process. These papers cover diverse issues, including privacy protection using Chaos-cryptography-based data scrambling and Markov chain algorithms, privacy filters in live surveillance video, preserving privacy in mobile video surveillance, and community-based user-specific and location-aware privacy awareness.

The first paper of this special issue “User centric privacy protection in video surveillance” by Thomas Winkler and Bernhard Rinner presents a concept for user-centric privacy awareness in video surveillance. The proposed system follows a community-based approach and empowers monitored persons to actively participate in registering cameras using their conventional smart phones. The collected information is used to warn users of violations of their personal privacy policy. Moreover, the proposed system is scalable in terms of different levels of privacy.

The second paper “A general framework for managing and processing live video data with privacy protection” by Alexander J. Aved and Kien A. Hua describes the live video database model with an intrinsic privacy model that provides a level of privacy protection not previously available for real-time streaming video data. The authors present the query language LVSQL, the system architecture, the object recognition and cross-camera tracking technique, and privacy filters. Privacy filters can be specified at different levels, i.e. users, cameras, and query.

The third paper “Chaos-cryptography based privacy preservation technique for video surveillance” by Sk. Md. Mizanur Rahman, M. Anwar Hossain, Hussein Mouftah, Abdulmotaleb El Saddik and Eiji Okamoto proposes a Chaos-cryptography-based data scrambling method to hide the privacy-sensitive regions of interest (ROI) in a surveillance video. The chaotic system makes use of Henon map as the logistic function, which generates the chaotic sequences. The chaotic sequences are in turn used to generate the bit sequences that controls the ROI scrambling. The proposed approach is computationally efficient and, hence, it can be applied for real-time video surveillance tasks in preserving privacy sensitive information.

The fourth paper “Intended human object detection for automatically protecting privacy in mobile video surveillance” by Yuta Nakashima, Noboru Babaguchi and Jianping Fan introduces a new concept called intended human objects that are defined as human objects essential for capture intentions, and develops a new method called intended human object detection that automatically detects the intended human objects in mobile surveillance videos. Through the process of intended human object detection, authors develop a system for automatically obscuring privacy sensitive regions.

The fifth and last paper of this issue “Privacy enabled video surveillance using a two state Markov tracking algorithm” by Peng Zhang, Tony Thomas and Sabu Emmanuel presents a novel, on-demand selectively revocable, privacy preserving mechanism for pedestrians in a surveillance video. In the proposed scheme, a surveillance video can be tuned to be viewed with complete privacy or by revoking the privacy of any subset of pedestrians while ensuring complete privacy to the remaining pedestrians. Authors achieve this by tracking the pedestrians using a novel Markov chain algorithm with two hidden states, detecting the head contour of the tracked pedestrians and obscuring their faces using an encryption mechanism. The detected pedestrian face/head is obscured by encrypting with a unique key derived from a master key for the privacy preservation purpose.

The guest editorial team thanks all the authors for submitting their quality work to this special issue, and to the numerous reviewers for their hard work and expert comments that proved highly useful for the success of this special issue. Our special thanks go to Prof. Thomas Plagemann, Editor-in-Chief for his invaluable guidance all through the process of this special issue.

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Pradeep K. Atrey
    • 1
  • Sabu Emmanuel
    • 2
  • Sharad Mehrotra
    • 3
  • Mohan S. Kankanhalli
    • 4
  1. 1.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Computer ScienceUniversity of CaliforniaIrvineUSA
  4. 4.School of ComputingNational University of SingaporeSingaporeSingapore

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