Hiding privacy information in video surveillance system

Wei, Zhang; Cheung, Susan; Chen, Minghua

doi:10.1109/icip.2005.1530530

Cited by 109 publications

(73 citation statements)

References 3 publications

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“…Different frameworks have been proposed to hide the data in the video itself, e.g., as a watermark [17] or as encrypted information in DCT blocks [18]. This information can be retrieved later on request.…”

Section: Related Workmentioning

confidence: 99%

“…15: Apply de-identification transformation on the segmented frames using one of the techniques mentioned in Section IV-C. 16: Apply the randomization kernel. 17: end if the corresponding image patch. The voting score for different positions and scales from multiple patches is minimized in a robust manner to combine the vote maps and select the most appropriate candidate.…”

Section: A Detect and Trackmentioning

confidence: 99%

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Person De-identification in Videos

Agrawal

Narayanan

2010

Computer Vision – ACCV 2009

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Abstract-Advances in cameras and web technology have made it easy to capture and share large amounts of video data over to a large number of people. A large number of cameras oversee public and semi-public spaces today. These raise concerns on the unintentional and unwarranted invasion of the privacy of individuals caught in the videos. To address these concerns, automated methods to de-identify individuals in these videos are necessary. De-identification does not aim at destroying all information involving the individuals. Its ideal goals are to obscure the identity of the actor without obscuring the action. This paper outlines the scenarios in which de-identification is required and the issues brought out by those. We also present an approach to de-identify individuals from videos. Our approach involves tracking and segmenting individuals in a conservative voxel space involving x, y, and time. A de-identification transformation is applied per frame using these voxels to obscure the identity. Face, silhouette, gait, and other characteristics need to be obscured, ideally. We show results of our scheme on a number of videos and for several variations of the transformations. We present the results of applying algorithmic identification on the transformed videos. We also present the results of a user-study to evaluate how well humans can identify individuals from the transformed videos.

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Section: Related Workmentioning

confidence: 99%

Section: A Detect and Trackmentioning

confidence: 99%

Person De-identification in Videos

Agrawal

Narayanan

2010

Computer Vision – ACCV 2009

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“…Chen et al built a system for people detection, tracking and recognition [11] using gait analysis, face detection and face recognition. As visual privacy is a significant concern of such systems, a number of researchers have explored how to integrate privacy systems into these surveillance systems [48,56,12,10,18]. Others such as Chinomi et al have evaluated different methods for obscuring people in video data [13].…”

Section: Related Workmentioning

confidence: 99%

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

Schiff

Meingast

Mulligan

et al. 2009

Protecting Privacy in Video Surveillance

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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 nine dimensional 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%. Fig. 1 A sample video frame is on left. The system has been trained to track green vests such as the one worn by the man with the outstretched arm. The system output is shown in the frame on the right, where an elliptical overlay hides the face of this man. The remainder of the scene including faces of workers not wearing green vests, remain visible. Note how the system successfully covers the face even when the vest is subjected to a shadow and a partial occlusion. Please visit "http://goldberg.berkeley.edu/RespectfulCameras" for more examples including video sequences.

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“…The technique in [11] removes privacy-sensitive information from the video sequence. A perceptually-based compressed-domain watermarking technique is then used to securely embed this data in the video stream.…”

Section: Introductionmentioning

confidence: 99%

A framework for the validation of privacy protection solutions in video surveillance

Dufaux

Ebrahimi

2010

2010 IEEE International Conference on Multimedia and Expo

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The issue of privacy protection in video surveillance has drawn a lot of interest lately. However, thorough performance analysis and validation is still lacking, especially regarding the fulfillment of privacy-related requirements. In this paper, we put forward a framework to assess the capacity of privacy protection solutions to hide distinguishing facial information and to conceal identity. We then conduct rigorous experiments to evaluate the performance of face recognition algorithms applied to images altered by privacy protection techniques. Results show the ineffectiveness of naïve privacy protection techniques such as pixelization and blur. Conversely, they demonstrate the effectiveness of more sophisticated scrambling techniques to foil face recognition.

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Hiding privacy information in video surveillance system

Cited by 109 publications

References 3 publications

Person De-identification in Videos

Person De-identification in Videos

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

A framework for the validation of privacy protection solutions in video surveillance

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