Francisco Flórez-RevueltaInterdisciplinary Hub for the Study of Health and Age-related conditions (IhSHA)

Visual monitoring of people in private spaces

Visual monitoring in publicspaces

Monitoring in private spaces

Cameras in private spaces

Fabien, C., Deepayan, B., Charith, A., & Mark, S. (2011). Video based technology forambient assisted living: A review of the literature. Journal of Ambient Intelligence andSmart Environments (JAISE), 3(3), 253-269

Chaaraoui, A. A., Climent-Pérez, P., & Flórez-Revuelta, F. (2012). A review on visiontechniques applied to human behaviour analysis for ambient-assisted living. ExpertSystems with Applications, 39(12), 10873-10888

Computer vision in AAL

Fall detection and prevention

Identification of Activities of DailyLiving

Behaviour monitoring

Physiological monitoring

Person-environment interaction

Rehabilitation

Support to people with cognitiveimpairment

Cons:Cluttered environmentsOcclusionsPrivacy preservationLimited field of view

Pros:Richer informationTracking of coarse and fine movements/actionsSynergies with other servicesEase to interpret

Pros and cons

Idea behind

Idea behind

Appropriate measures need to be considered to maintain privacy and increaseacceptance

The notion of privacy is highly subjective. It depends on theindividual.

Several factors are involved:The private “thing”The observer

An image conveys the following information about an individual:Identity (Who?)Appearance (How?)Activity / Behaviour / Event (What?)Time (When?)Location (Where?)

Privacy preservation

Image Redaction: Modify an image or a sequence of images so as to protectobjects (visual clues) appearing on them

But…The image must retain its utility

A trade-off between privacy protection and image utility is needed

Privacy must be adaptable to the individual

Ensuring visual privacy

We propose a privacy protection scheme that is aware of the context

A set of redaction methods is used

A context describes “any” situation:Identity of the observerIdentity of the observed person (to retrieve the privacy profile)Closeness between person and observer, e.g. relative, doctor, neighbourAppearance (dressed?)Location, e.g. kitchen, living roomEvent, e.g. cooking, watching TV, fall

Users provide their privacy preferences by linking instances of the context withprotection/visualisation methods

Privacy by context

Privacy by context

Visualisation levels

Original Pixelate Blur Emboss Silhouette

Skeleton Avatar Invisibility

Visualisation levels

Tests with RGB-D cameras

Tests with RGB cameras

Improvements in the calculation of the contextIdentity (Who?)Appearance (How?)Activity / Behaviour / Event (What?)Time (When?)Location (Where?)

Improvements in foreground/person detection

Most of activity/behaviour identification systems are validated in labs, not in realenvironments

Privacy of the environment

Main issues: current and futurework

Padilla-López, J.R.; Chaaraoui, A.A.; Gu, F.; Flórez-Revuelta, F. (2015). Visualprivacy by context: proposal and evaluation of a level-based visualisationscheme. Sensors, 15(6):12959-12982.

Padilla-López, J.R.; Chaaraoui, A.A.; Flórez-Revuelta, F. (2015). Visual privacyprotection methods: A survey. Expert Systems With Applications, 42(9): 4177-4195.

Flórez-Revuelta, F.; Gu. F.; Pierscionek, B.; Remagnino, P. (2015). White paperon AAL systems and associated privacy issues. Public report, BREATHEConsortium.

Padilla-López, J.R.; Flórez-Revuelta, F.; Monekosso, D.N.; Remagnino, P.(2012). The “Good” Brother: Monitoring People Activity in Private Spaces. InDistributed Computing and Artificial Intelligence, pp. 49-56, Springer.

http://www.breathe-project.eu/

More information

Francisco (Paco) Flórez-RevueltaF.Florez@kingston.ac.uk @fflorezrevueltastaffnet.kingston.ac.uk/~ku48824 franciscoflorezrevuelta