Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Enabling Video Privacy through Computer Vision
IEEE Security and Privacy
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A novel approach for privacy-preserving video sharing
Proceedings of the 14th ACM international conference on Information and knowledge management
Graphical Models
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Face recognition from video using the generic shape-illumination manifold
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Fast and reliable active appearance model search for 3-D face tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Negotiating privacy preferences in video surveillance systems
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Adaptive transformation for robust privacy protection in video surveillance
Advances in Multimedia
Multimedia Tools and Applications
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This paper presents a framework for preserving privacy in video surveillance. Raw video is decomposed into a background and one or more object-video streams. Object-video 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.