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Visual Privacy by Context:A Level-Based Visualisation
Scheme
José R. Padilla-López, Alexandros A. Chaaraoui and Francisco Flórez-Revuelta
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Protection methods
Naïve Blurring Pixelating Emboss
Silhouette / Masking Skeleton 3D Avatar Invisibility
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Why (visual) privacy?
AAL services provide help to people in need of long-term care.
Development of novel video-based AAL services for private spaces.
Some AAL services may require human access to video stream.
...
...
...
Long-term analysis
Setup and Profiles DB (Activities, Inhabitants, Objects, ...)
Log
Alarm Actuators
Event
Caregiver
Camera 1 Camera 2 Camera N
Motion Detection
Motion Detection
Motion Detection
Human Behaviour
Analysis
Human Behaviour
Analysis
Human Behaviour
Analysis
Multi-view Human Behaviour Analysis
Environmental Sensor Information
Reasoning System
Privacy
Architecture of our Intelligent Monitoring System
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but... What is privacy?
Privacy: Sphere of the private life that an individual has right to protect from intruders.
However...
The notion of privacy is highly subjective. It depends on the individual.
Several factors are involved:
● The private “thing”
● Observer / intruder
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The private “thing”
“A picture is worth a thousand words”
An image conveys the following information about individuals:
● Identity (Who?)
● Appearance (How?)
● Location (Where?)
● Activity / Behaviour (What?)
● Time (When?)
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How to ensure (visual) privacy?
But...
● Image must retain its utility
● A trade-off between privacy protection and image utility is needed
● Privacy must be adaptable to the individual
Image Redaction: Modify an image or a sequence of images so as to protect objects (visual clues) appearing on them.
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Privacy by Context
● We propose a privacy protection scheme that is aware of the context
● A set of redaction methods is used
● A context describes “any” situation.
● Users provide their privacy preferences by linking instances of the context with protection methods
Blur
Pixel
Emboss
Silhouette
Skeleton Avatar 3D
Naïve
Invisibility
Selected Method Skeleton
Set of redaction methods
User-given Matching
Context Composed of several
variables
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Used Context
● The observer / viewer / watcher
● Identity of the person (to retrieve the privacy profile)
● Closeness between person and observer (e.g. relative, doctor, acquaintance, … )
● Appearance (dressed?)
● Location (e.g. kitchen)
● Event (e.g. cooking, watching TV, fall, ...)
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Redaction methods
Demo
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Conclusions
● Visual Privacy by Context covers several research fields Computer vision (HBA, Object detection, Person Re-id, …), Image processing and in-painting, AI, Sociology.
● As privacy is highly subjective, any solution should be adaptable to each individual
● Need of a privacy measure so as to objectively evaluate protection methods
Thank you for your attention
Questions?