A general framework for managing and processing live video data with privacy protection |
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Authors: | Alexander J Aved Kien A Hua |
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Affiliation: | (1) University of Central Florida, Orlando, FL, USA |
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Abstract: | Though a large body of existing work on video surveillance focuses on image and video processing techniques, few address the
usability of such systems, and in particular privacy issues. This study fuses concepts from stream processing and content-based
image retrieval to construct a privacy-preserving framework for rapid development and deployment of video surveillance applications.
Privacy policies, instantiated to as privacy filters, may be applied both granularly and hierarchically. Privacy filters are
granular as they are applicable to specific objects appearing in the video streams. They are hierarchal because they can be
specified at specific objects in the framework (e.g., users, cameras) and are combined such that the disseminated video stream
adheres to the most stringent aspect specified in the cascade of all privacy filters relevant to a video stream or query.
To support this privacy framework, we extend our Live Video Database Model with an informatics-based approach to object recognition
and tracking and add an intrinsic privacy model that provides a level of privacy protection not previously available for real-time
streaming video data. The proposed framework also provides a formal approach to implement and enforce privacy policies that
are verifiable, an important step towards privacy certification of video surveillance systems through a standardized privacy
specification language. |
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Keywords: | |
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