A Framework for Privacy (by Design) for Video Surveillance Systems

Visual Computing Lab
Faculty of Science
University of Ontario Institute of Technology
2000 Simcoe St. N., Oshawa ON L1G 0C5


We presents a framework for preserving privacy in video surveillance. Raw video is decomposed into a background and one or more object-video streams. Objectvideo streams can be combined to render the scene in a variety of ways: 1) The original video can be reconstructed from object-video streams without any data loss; 2) individuals in the scene can be represented as blobs, obscuring their identities; 3) foreground objects can be color coded to convey subtle scene information to the operator, again without revealing the identities of the individuals present in the scene; 4) the scene can be partially rendered, i.e., revealing the identities of some individuals, while preserving the anonymity of others. We evaluate our approach in a virtual train station environment populated by autonomous, lifelike virtual pedestrians.



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