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Privacy Preserving Real-Time Video Stream Change Detection Based on the Orthogonal Tensor Decomposition Models

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Research Challenges in Information Science (RCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

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Abstract

In this paper the video change detection method that allows for data privacy protection is proposed. Signal change detection is based on the tensor models constructed in the orthogonal tensor subspaces. Tensor methods allow for processing of any kind of multi-dimensional signals since computation of special features is not required. The proposed signal encoding method makes that person identification in the processed signal is very difficult or impossible for the unauthorized personnel. It is demonstrated that despite the input being distorted for encryption, the proposed tensor based method can still correctly identify video shots in real-time. Compared with the non-distorted signals, the obtained accuracy is only slightly lower, at the same time providing data privacy.

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Acknowledgement

This work was supported by the Polish National Science Center NCN under the grant no. 2016/21/B/ST6/01461.

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Authors and Affiliations

  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

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  1. Bogusław Cyganek
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Correspondence to Bogusław Cyganek .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

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Cyganek, B. (2020). Privacy Preserving Real-Time Video Stream Change Detection Based on the Orthogonal Tensor Decomposition Models. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_29

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  • DOI: https://doi.org/10.1007/978-3-030-50316-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50315-4

  • Online ISBN: 978-3-030-50316-1

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