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Shadow Detection for Increased Accuracy of Privacy Enhancing Methods in Video Surveillance Edge Devices

Authors: F. Matusek, G. Pujolle, R. Reda


Shadow detection is still considered as one of the potential challenges for intelligent automated video surveillance systems. A pre requisite for reliable and accurate detection and tracking is the correct shadow detection and classification. In such a landscape of conditions, privacy issues add more and more complexity and require reliable shadow detection. In this work the intertwining between security, accuracy, reliability and privacy is analyzed and, accordingly, a novel architecture for Privacy Enhancing Video Surveillance (PEVS) is introduced. Shadow detection and masking are dealt with through the combination of two different approaches simultaneously. This results in a unique privacy enhancement, without affecting security. Subsequently, the methodology was employed successfully in a large-scale wireless video surveillance system; privacy relevant information was stored and encrypted on the unit, without transferring it over an un-trusted network.

Keywords: Privacy, Physical Security, Video Surveillance, shadow detection, Intelligent Video Surveillance, WSSU

Digital Object Identifier (DOI):

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[1] F. Matusek, S. Sutor, K. Kraus, F. Kruse and R. Reda, NIVSS: A Nearly Indestructible Video Surveillance System, The Third International Conference on Internet Monitoring and Protection, Bucharest, July 2008.
[2] A. Prati, I. Mikic, M.M. Trivedi and R. Cucchiara. Detecting Moving Shadows: Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25: 918 - 923, July 2003.
[3] A. Prati, I. Mikic, R. Cucchiara and M.M. Trivedi. Comparative Evaluation of Moving Shadow Detection Algorithms. Proceedings of 3rd Workshop on Empirical Evaluation in Computer Vision (in conjunction with CVPR 2001), December 2001.
[4] T. Horprasert, D. Harwood and L.S. Davis. A statistical approach for real-time robust background subtraction and shadow detection. Proceedings of IEEE ICCV'99 FRAME-RATE Workshop, 1999.
[5] S. Sutor, F. Matusek, and R. Reda, "WSSU: Wireless Self-Contained Surveillance Unit; An Ad-Hoc Video Surveillance System", in Press, presented at The Fourth Advanced International Conference on Telecommunications, Athens, June, 2008.
[6] F. Matusek and R. Reda, "Efficient and Secure Storage of Privacy Enhanced Video Surveillance Data in Intelligent Video Surveillance Systems", to be presented at the International Symposium on Computer and Information Sciences, October 2008.
[7] F. Matusek and R Reda, VSLC: Video Surveillance Network Control, Mobile Video Surveillance Local Control Engineering and Applications to be presented at IEEE, IFIP Wireless Days Conference 2008.
[8] S. Nadimi and B. Bhanu. Physical models for moving shadow and object detection in video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:1079 - 1087, August 2004.
[9] Y. Cheng, "Mean Shift, Mode Seeking and Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, pp. 790-799, 1995.
[10] P. Viola, and Michael Jones. "Rapid Object Detection using a Boosted Cascade of Simple Features," cvpr, p. 511, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001.
[11] C. Stauffer and E. Grimson. Adaptive background mixture models for real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:747 - 757, August 2000.