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Protecting Privacy in Video Surveillance offers the state of the art from leading researchers and experts in the field. This broad ranging volume discusses the topic from various technical points of view and also examines surveillance from a societal perspective.

A comprehensive introduction carefully guides the reader through the collection of cutting-edge research and current thinking. The technical elements of the field feature topics from MERL blind vision, stealth vision and privacy by de-identifying face images, to using mobile communications to assert privacy from video surveillance, and using wearable computing devices for data collection in surveillance environments. Surveillance and society is approached with discussions of security versus privacy, the rise of surveillance, and focusing on social control.

This rich array of the current research in the field will be an invaluable reference for researchers, as well as graduate students.

Inhaltsverzeichnis

Frontmatter

An Introduction to Automatic Video Surveillance

We present a brief summary of the elements in an automatic video surveillance system, from imaging system to metadata. Surveillance system architectures are described, followed by the steps in video analysis, from preprocessing to object detection, tracking, classification and behaviour analysis.
Andrew Senior

Protecting and Managing Privacy Information in Video Surveillance Systems

Recent widespread deployment and increased sophistication of video surveillance systems have raised apprehension of their threat to individuals’ right of privacy. Privacy protection technologies developed thus far have focused mainly on different visual obfuscation techniques but no comprehensive solution has yet been proposed. We describe a prototype system for privacy-protected video surveillance that advances the state-of-the-art in three different areas: First, after identifying the individuals whose privacy needs to be protected, a fast and effective video inpainting algorithm is applied to erase individuals’ images as a means of privacy protection. Second, to authenticate this modification, a novel rate-distortion optimized data-hiding scheme is used to embed the extracted private information into the modified video. While keeping the modified video standard-compliant, our data hiding scheme allows the original data to be retrieved with proper authentication. Third, we view the original video as a private property of the individuals in it and develop a secure infrastructure similar to a Digital Rights Management system that allows individuals to selectively grant access to their privacy information.
S.-C.S. Cheung, M.V. Venkatesh, J.K. Paruchuri, J. Zhao, T. Nguyen

Privacy Protection in a Video Surveillance System

This chapter presents mechanisms for privacy protection in a distributed, multicamera surveillance system. The design choices and alternatives for providing privacy protection while delivering meaningful surveillance data for security and retail environments are described, followed by performance metrics to evaluate theeffectiveness of privacy protection measures and experiments to evaluate these in retail store video. This chapter concludes with a discussion including five principles for data presentation of privacy protection systems.
Andrew Senior

Oblivious Image Matching

Video surveillance is an intrusive operation that violates privacy. It is therefore desirable to devise surveillance protocols that minimize or even eliminate privacy intrusion. A principled way of doing so is to resort to Secure Multi-Party methods, that are provably secure, and adapt them to various vision algorithms. In this chapter, we describe an Oblivious Image Matching protocol which is a secure protocol for image matching. Image matching is a generalization of detection and recognition tasks since detection can be viewed as matching a particular image to a given object class (i.e., does this image contain a face?) while recognition can be viewed as matching an image of a particular instance of a class to another image of the same instance (i.e., does this image contain a particular car?). And instead of applying the Oblivious Image Matching to the entire image one can apply it to various sub-images, thus solving the localization problem (i.e., where is the gun in the image?). A leading approach to object detection and recognition is the bag-offeatures approach, where each object is reduced to a set of features and matching objects is reduced to matching their corresponding sets of features. Oblivious Image Matching uses a secure fuzzy match of string and sets as its building block. In the proposed protocol, two parties, Alice and Bob, wish to match their images, without leaking additional information. We use a novel cryptographic protocol for fuzzy matching and adopt it to the bag-of-features approach. Fuzzy matching compares two sets (or strings) and declares them to match if a certain percentage of their elements match. To apply fuzzy matching to images, we represent images as a set of visual words that can be fed to the secure fuzzy matching protocol. The fusion of a novel cryptographic protocol and recent advances in computer vision results in a secure and efficient protocol for image matching. Experiments on real images are presented.
Shai Avidan, Ariel Elbaz, Tal Malkin, Ryan Moriarty

Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns

To address privacy concerns regarding digital video surveillance cameras, we propose a practical, real-time approach that preserves the ability to observe actions while obscuring individual identities. In the Respectful Cameras system, people who wish to remain anonymous wear colored markers such as hats or vests. The system automatically tracks these markers using statistical learning and classification to infer the location and size of each face. It obscures faces with solid ellipsoidal overlays, while minimizing the overlay area to maximize the remaining observable region of the scene. Our approach uses a visual color-tracker based on a 9D color-space using a Probabilistic Adaptive Boosting (AdaBoost) classifier with axis-aligned hyperplanes as weak hypotheses. We then use Sampling Importance Resampling (SIR) Particle Filtering to incorporate interframe temporal information. Because our system explicitly tracks markers, our system is well-suited for applications with dynamic backgrounds or where the camera can move (e.g., under remote control). We present experiments illustrating the performance of our system in both indoor and outdoor settings, with occlusions, multiple crossing targets, lighting changes, and observation by a moving robotic camera. Results suggest that our implementation can track markers and keep false negative rates below 2%.
Jeremy Schiff, Marci Meingast, Deirdre K. Mulligan, Shankar Sastry, Ken Goldberg

Technical Challenges in Location-Aware Video Surveillance Privacy

Though designing, deploying, and operating a video surveillance system in a public place is a relatively simple engineering task, equipping operational systems with privacy enhancing technology presents extraordinarily difficult technical challenges. We explore using mobile communications and location tracking to enable individuals to assert a preference for privacy from video surveillance. Rather than prohibit or defeat surveillance, our system – Cloak – seeks to discourage surveillers from distributing video without the authorization of the surveilled. We review the system architecture and operation, and demonstrate how privacy can be enhanced while requiring no change to existing surveillance technology. We use analysis and simulation to explore the solution’s feasibility, and show that an individual’s video privacy can be protected even in the presence of the many sources of error (e.g., dense crowds, unsynchronized clocks, unreliable communications, location error, location signal loss) we anticipate in a deployed system. Finally, we discuss the key technical, social, and legal barriers to Cloak’s large-scale deployment, and argue that the pervasive use of camera phones requires the focus of efforts on surveillance privacy technology to shift to limiting dissemination rather than limiting video capture.
Jack Brassil

Protecting Personal Identification in Video

In this chapter, we present some studies on protecting personal identification in video. First, we discuss and evaluate automatic face masking techniques for obscuring human faces in video. Second, a user study is presented to reveal that face-masked video can be attacked using pair-wise constraints. Next, we propose an algorithm to show that this type of pair-wise constraint attack can be implemented using state-of-the-art machine learning approaches. Finally, a new obscuring approach is proposed to avoid the pair-wise constraint attack. The proposed approach protects people’s identity by obscuring the texture information of the entire body.
Datong Chen, Yi Chang, Rong Yan, Jie Yang

Face De-identification

With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. In most of these applications, knowledge of the identity of people in the image is not required. This makes the case for image de-identification, the removal of identifying information from images, prior to sharing of the data. Privacy protection methods are well established for field-structured data; however, work on images is still limited. In this chapter, we review previously proposed naïve and formal face de-identification methods. We then describe a novel framework for the de-identification of face images using multi-factor models which unify linear, bilinear, and quadratic data models. We show in experiments on a large expression-variant face database that the new algorithm is able to protect privacy while preserving data utility. The new model extends directly to image sequences, which we demonstrate on examples from a medical face database.
Ralph Gross, Latanya Sweeney, Jeffrey Cohn, Fernando de la Torre, Simon Baker

Psychological Study for Designing Privacy Protected Video Surveillance System: PriSurv

Abstract As video surveillance systems are widely deployed, concerns continue to grow about invasion of privacy. We have built a privacy protected video surveillance system called PriSurv. Although PriSurv protects subject privacy using image processing, criteria of controlling the subject’s visual information that is privacy-sensitive should be clarified. Visual information must be disclosed by considering the trade-off between privacy and security. The level of privacy-sensitive visual information that could be disclosed to a viewer is simply called disclosable privacy in this chapter. Disclosable privacy, which deeply involves the personal sense, is affected by many factors. A sense of privacy is individual, but in some cases it might have common factors. A sense of privacy is individual, but in some cases it might have common factors. In this chapter, we analyze what factors determine and affect disclosable privacy by applying statistical analysis to questionnaire-based experimental results. These results indicate that disclosable privacy is concerned with how much a subject has feeling of closeness to a viewer and expects the viewer’s responsibility. They also show that disclosable privacy differs greatly by individuals. Reflecting the obtained findings in PriSurv’s design, we adapt PriSurv to reflect a personal sense of privacy.
Noboru Babaguchi, Takashi Koshimizu, Ichiro Umata, Tomoji Toriyama

Selective Archiving: A Model for Privacy Sensitive Capture and Access Technologies

At times, people need or want a record of their previous experiences. Sometimes those records are media other than text-based descriptions or notes. At the same time, a world of constant capture invokes Orwellian fears of surveillance and monitoring in a modern digital Panopticon. Thus, the selective archiving model, in which data are constantly buffered but require explicit input to be archived, represents a compromise through which people can dynamically negotiate their own policies around control, privacy, information access, and comfort. Through multiple formative studies and two deployment studies of selective archiving technologies in very different spaces for very different reasons, we are able to tease out some significant themes about recording in everyday life. In this chapter, we discuss those issues as observed in this work and outline some areas of future research in selective archiving.
Gillian R. Hayes, Khai N. Truong

BlindSpot: Creating Capture-Resistant Spaces

The increasing presence of digital cameras and camera phones brings with it legitimate concerns of unwanted recording situations for many organizations and individuals. Although the confiscation of these devices from their owners can curb the capture of sensitive information, it is neither a practical nor desirable solution. In this chapter, we present the design of a system, called BlindSpot, which prevents the recording of still and moving images without requiring any cooperation on the part of the capturing device or its operator. Our solution involves a simple tracking system for locating any number of retro-reflective CCD or CMOS camera lenses around a protected area. The system then directs a pulsing light at the lens, distorting any imagery the camera records.
Shwetak N. Patel, Jay W. Summet, Khai N. Truong

Backmatter

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