Performance Study of a Selective Encryption Scheme for the Security of Networked, Real-Time Video
ICCCN '95 Proceedings of the 4th International Conference on Computer Communications and Networks
Convex Optimization
ACM Computing Surveys (CSUR)
Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust particle filtering for object tracking
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
IEEE Transactions on Information Theory
Efficient background subtraction for real-time tracking in embedded camera networks
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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In a typical video analysis framework, video sequences are decoded and reconstructed in the pixel domain before being processed for high level tasks such as classification or detection.Nevertheless, in some application scenarios, it might be of interest to complete these analysis tasks without disclosing sensitive data, e.g. the identity of people captured by surveillance cameras. In this paper we propose a new coding scheme suitable for video surveillance applications that allows tracking of video objects without the need to reconstruct the sequence,thus enabling privacy protection. By taking advantage of recent findings in the compressive sensing literature, we encode a video sequence with a limited number of pseudo-random projections of each frame. At the decoder, we exploit the sparsity that characterizes background subtracted images in order to recover the location of the foreground object. We also leverage the prior knowledge about the estimated location of the object, which is predicted by means of a particle filter, to improve the recovery of the foreground object location. The proposed framework enables privacy, in the sense it is impossible to reconstruct the original video content from the encoded random projections alone, as well as secrecy, since decoding is prevented if the seed used to generate the random projections is not available.