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Intelligent Multimedia Surveillance pp 67–82Cite as

Object Video Streams: A Framework for Preserving Privacy in Video Surveillance

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Abstract

Here we introduce a framework for preserving privacy in video surveillance. Raw video footage is decomposed into a background and one or more object-video streams. Such object-centric decomposition of the incoming video footage opens up new possibilities to provide visual surveillance of an area without compromising the privacy of the individuals present in that area. Object-video streams allow us to render the scene in a variety of ways: (1) individuals in the scene can be represented as blobs, obscuring their identities; (2) 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; (3) the scene can be partially rendered, that is, revealing the identities of some individuals, while preserving the anonymity of others, etc. We evaluate our approach in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. We also demonstrate our approach on real video footage. Lastly, we show that Microsoft Kinect sensor can be used to decompose the incoming video footage into object-video streams.

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Notes

  1. 1.

    This chapter is based upon our paper that appeared in the 6th International Conference on Advanced Video and Signal Based Surveillance in 2009 [16].

  2. 2.

    This assumption sometimes breaks due to the limitations of video processing routines, such as background subtraction, object tracking, image segmentation, etc. Still under favorable conditions—good lighting, sparsely populated scenes, etc.—it is possible to decompose the video into object-video streams as we show later in the chapter.

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Acknowledgements

We thank Wei Shao and Mauricio Plaza-Villegas for their invaluable contributions to the implementation of the Penn Station simulator. We also thank Jordan Stadler for his work on constructing object-video streams using Microsoft Kinect sensor. This work is supported in part by the UOIT Startup Fund. We also acknowledge the NSERC Discovery Grant program.

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Authors and Affiliations

  1. Faculty of Science, University of Ontario Institute of Technology, Oshawa, ON, Canada

    Faisal Z. Qureshi

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  1. Faisal Z. Qureshi
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Correspondence to Faisal Z. Qureshi .

Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, Canada

    Pradeep K. Atrey

  2. Dept. of Computer Science, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Elec. Engineering and Computer Science, Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

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Qureshi, F.Z. (2013). Object Video Streams: A Framework for Preserving Privacy in Video Surveillance. In: Atrey, P., Kankanhalli, M., Cavallaro, A. (eds) Intelligent Multimedia Surveillance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41512-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-41512-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41511-1

  • Online ISBN: 978-3-642-41512-8

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