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Effective evaluation of privacy protection techniques in visible and thermal imagery
Computational Vision Group, Department of Computer Science, University of Reading.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.ORCID iD: 0000-0002-6591-9400
Computational Vision Group, Department of Computer Science, University of Reading.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.ORCID iD: 0000-0002-6763-5487
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2017 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 26, no 5, article id 051408Article in journal (Refereed) Published
Abstract [en]

Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This work proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset, called TST-Priv, is made available online below for community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of the proposed method over existing methods.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2017. Vol. 26, no 5, article id 051408
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-140495DOI: 10.1117/1.JEI.26.5.051408ISI: 000414251400009OAI: oai:DiVA.org:liu-140495DiVA, id: diva2:1138417
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784
Note

Funding agencies:  Swedish Research Council through the project Learning Systems for Remote Thermography [D0570301]; European Community [312784]

Available from: 2017-09-05 Created: 2017-09-05 Last updated: 2018-01-13Bibliographically approved

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Berg, AmandaAhlberg, JörgenFelsberg, Michael

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CiteExportLink to record
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