Visual privacy by context: Proposal and evaluation of a level-based visualisation scheme

Sensors (Switzerland)

Volumn 15, Issue 6, 2015, Pages 12959-12982

  Padilla López, José Ramón ^a   Chaaraoui, Alexandros Andre ^a   Gu, Feng ^b   Flórez Revuelta, Francisco ^a,b  

^a UNIVERSITY OF ALICANTE   (Spain)

^b KINGSTON UNIVERSITY   (United Kingdom)

Author keywords

Ambient assisted living; Context; Intelligent monitoring; Privacy

Indexed keywords

CAMERAS; COMPUTER VISION; DATA PRIVACY;

AMBIENT ASSISTED LIVING; CONTEXT; DEGREE OF PROTECTION; HUMAN INTERVENTION; INTELLIGENT MONITORING; LEVELS OF PROTECTIONS; SAFETY ASSESSMENTS; TELEREHABILITATION;

VISUALIZATION;

EID: 84930959033     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150612959     Document Type: Article

References (51)

1. Seniorwatch 2. Assessment of the Senior Market for ICT Progress and Developments; Technical Report; European Commission, Information Society and Media Directorate General: Brussels, Belgium, 2008.
2. Overview of the European strategy in ICT for Ageing Well; Technical Report; European Commission: Brussels, Belgium, 2009.
3. Kleinberger, T.; Becker, M.; Ras, E.; Holzinger, A.; Müller, P. Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces. In Universal Access in Human-Computer Interaction. Ambient Interaction; Stephanidis, C., Ed.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2007; Volume 4555, pp. 103-112.
4. Röcker, C.; Ziefle, M.; Holzinger, A. Social inclusion in AAL environments: Home automation and convenience services for elderly users. In Proceedings of the International Conference on Artificial Intelligence (ICAI2011), Puebla, Mexico, 26 November-4 December 2011; CSERA Press: New York, NY, USA, 2011; pp. 55-59.
5. Remagnino, P.; Foresti, G.L.; Ellis, T. (Eds.) Ambient Intelligence: A Novel Paradigm; Springer: New York, NY, USA, 2005.
6. Fleck, S.; Strasser, W. Smart Camera Based Monitoring System and Its Application to Assisted Living. IEEE Proc. 2008, 96, 1698-1714.
7. Chaaraoui, A.A.; Climent-Pérez, P.; Flórez-Revuelta, F. A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living. Expert Syst. Appl. 2012, 39, 10873-10888.
8. Cardinaux, F.; Bhowmik, D.; Abhayaratne, C.; Hawley, M.S. Video Based Technology for Ambient Assisted Living: A Review of the Literature. J. Ambient Intell. Smart Environ. 2011, 3, 253-269.
9. Ziefle, M.; Rocker, C.; Holzinger, A. Medical Technology in Smart Homes: Exploring the User's Perspective on Privacy, Intimacy and Trust. In Proceedings of the 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops (COMPSACW), Munich, Germany, 18-22 July 2011; pp. 410-415.
10. Senior, A.; Pankanti, S. Privacy Protection and Face Recognition. In Handbook of Face Recognition; Li, S.Z., Jain, A.K., Eds.; Springer: London, UK, 2011; pp. 671-691.
11. Schaar, P. Privacy by Design. Identity Inf. Soc. 2010, 3, 267-274.
12. Ziefle, M.; Klack, L.; Wilkowska, W.; Holzinger, A. Acceptance of Telemedical Treatments-A Medical Professional Point of View. In Human Interface and the Management of Information. Information and Interaction for Health, Safety, Mobility and Complex Environments; Yamamoto, S., Ed.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2013; Volume 8017, pp. 325-334.
13. Cook, D.J.; Augusto, J.C.; Jakkula, V.R. Ambient intelligence: Technologies, applications, and opportunities. Pervasive Mob. Comput. 2009, 5, 277-298.
14. Cavoukian, A. Surveillance, Then and Now: Securing Privacy in Public Spaces; Technical Report; Information and Privacy Commmissioner of Ontario, Toronto, ON, Canada, 2013.
15. Winkler, T.; Rinner, B. Security and Privacy Protection in Visual Sensor Networks: A Survey. ACM Comput. Surv. 2014, 47, 1-42.
16. Padilla-López, J.R.; Chaaraoui, A.A.; Flórez-Revuelta, F. Visual privacy protection methods: A survey. Expert Syst. Appl. 2015, 42, 4177-4195.
17. Wagstaff, J. Using Bluetooth To Disable Camera Phones, 2004. Available online: http://www.loosewireblog.com/2004/09/using_bluetooth.html (accessed on 3 June 2015).
18. Patel, S.N.; Summet, J.W.; Truong, K.N. BlindSpot: Creating Capture-Resistant Spaces. In Protecting Privacy in Video Surveillance; Senior, A., Ed.; Springer: London, UK, 2009; pp. 185-201.
19. Harvey, A. Camoflash-Anti-Paparazzi Clutch, 2010. Available online: http://ahprojects.com/ projects/camoflash/ (accessed on 3 June 2015).
20. Avidan, S.; Butman, M. Blind Vision. In Computer Vision-ECCV 2006; Leonardis, A., Bischof, H., Pinz, A., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2006; Volume 3953, pp. 1-13.
21. Erkin, Z.; Franz, M.; Guajardo, J.; Katzenbeisser, S.; Lagendijk, I.; Toft, T. Privacy-Preserving Face Recognition. In Privacy Enhancing Technologies; Goldberg, I., Atallah, M., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2009; Volume 5672, pp. 235-253.
22. Shashank, J.; Kowshik, P.; Srinathan, K.; Jawahar, C. Private Content Based Image Retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, AK, USA, 24-26 June 2008; pp. 1-8.
23. Frome, A.; Cheung, G.; Abdulkader, A.; Zennaro, M.; Bo, W.; Bissacco, A.; Adam, H.; Neven, H.; Vincent, L. Large-scale privacy protection in Google Street View. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 27 September-4 October 2009; pp. 2373-2380.
24. Dufaux, F. Video scrambling for privacy protection in video surveillance: Recent results and validation framework. Proc. SPIE 2011, 8063, 1-12.
25. Gross, R.; Sweeney, L.; Cohn, J.; Torre, F.; Baker, S. Face De-identification. In Protecting Privacy in Video Surveillance; Senior, A., Ed.; Springer: London, UK, 2009; pp. 129-146.
26. Ghanbari, A.; Soryani, M. Contour-Based Video Inpainting. In Proceedings of the 2011 IEEE 7th Iranian Machine Vision and Image Processing (MVIP), Tehran, Iran, 16-17 November 2011; pp. 1-5.
27. Chinomi, K.; Nitta, N.; Ito, Y.; Babaguchi, N. PriSurv: Privacy Protected Video Surveillance System Using Adaptive Visual Abstraction. In Proceedings of the 14th International Conference on Advances in Multimedia Modeling (MMM'08), Kyoto, Japan, 9-11 January 2008; Springer: Berlin/Heidelberg, Germany, 2008; pp. 144-154.
28. Petitcolas, F.; Anderson, R.; Kuhn, M. Information hiding-A survey. IEEE Proc. 1999, 87, 1062-1078.
29. Cheung, S.C.; Venkatesh, M.; Paruchuri, J.; Zhao, J.; Nguyen, T. Protecting and Managing Privacy Information in Video Surveillance Systems. In Protecting Privacy in Video Surveillance; Senior, A., Ed.; Springer: London, UK, 2009; pp. 11-33.
30. Korshunov, P.; Araimo, C.; de Simone, F.; Velardo, C.; Dugelay, J.; Ebrahimi, T. Subjective study of privacy filters in video surveillance. In Proceedings of the 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), Banff, AB, Canada, 17-19 September 2012; pp. 378-382.
31. 3-privacy: Understanding what, when, and where inference channels in multi-camera surveillance video. Multimed. Tools Appl. 2014, 68, 135-158.
32. Neustaedter, C.; Greenberg, S.; Boyle, M. Blur Filtration Fails to Preserve Privacy for Home-Based Video Conferencing. ACM Trans. Comput.-Hum. Interact. 2006, 13, 1-36.
33. Badii, A.; Einig, M.; Tiemann, M.; Thiemert, D.; Lallah, C. Visual context identification for privacy-respecting video analytics. In Proceedings of the 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), Banff, AB, Canada, 17-19 September 2012; pp. 366-371.
34. Padilla-López, J.R.; Chaaraoui, A.A.; Flórez-Revuelta, F. Visual Privacy by Context: A Level-Based Visualisation Scheme. In Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services; Hervás, R., Lee, S., Nugent, C., Bravo, J., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Zug, Switzerland, 2014; Volume 8867, pp. 333-336.
35. Chaaraoui, A.A.; Padilla-López, J.R.; Ferrández-Pastor, F.J.; Nieto-Hidalgo, M.; Flórez-Revuelta, F. A vision-based system for intelligent monitoring: Human behaviour analysis and privacy by context. Sensors 2014, 14, 8895-8925.
36. AALIANCE2 Roadmap. Technical report, European Next Generation Ambient Assisted Living Innovation Alliance, 2013. Available online: http://www.aaliance2.eu/ (accessed on 3 June 2015).
37. Kitahara, I.; Kogure, K.; Hagita, N. Stealth Vision for Protecting Privacy. In Proceedings of 17th International Conference on the Pattern Recognition (ICPR'04), Cambridge, UK, 23-26 August 2004; IEEE Computer Society: Washington, DC, USA, 2004; pp. 404-407.
38. Erdélyi, A.;Winkler, T.; Rinner, B. Serious Fun: Cartooning for Privacy Protection. In Proceedings of the MediaEval Workshop, Barcelona, Spain, 18-19 October 2013; Volume 2.
39. Koshimizu, T.; Toriyama, T.; Babaguchi, N. Factors on the Sense of Privacy in Video Surveillance. In Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrival of Personal Experences (CARPE'06); ACM: New York, NY, USA, 2006; pp. 35-44.
40. Agrawal, P.; Narayanan, P.J. Person De-Identification in Videos. IEEE Trans. Circuits Syst. Video Technol. 2011, 21, 299-310.
41. Chen, Y.; Dang, G.; Cheng, Z.; Xu, K. Fast capture of personalized avatar using two Kinects. J. Manuf. Syst. 2014, 33, 233-240.
42. Chaaraoui, A.A.; Padilla-López, J.R.; Climent-Pérez, P.; Flórez-Revuelta, F. Evolutionary joint selection to improve human action recognition with RGB-D devices. Expert Syst. Appl. 2014, 41, 786-794.
43. Horprasert, T.; Harwood, D.; Davis, L.S. A statistical approach for real-time robust background subtraction and shadow detection. IEEE ICCV 1999, 99, 1-19.
44. Piccardi, M. Background subtraction techniques: A review. In Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics, The Hague, The Netherlands, 10-13 October 2004; Volume 4, pp. 3099-3104.
45. Kim, Z. Real time object tracking based on dynamic feature grouping with background subtraction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, AK, USA, 24-26 June 2008; pp. 1-8.
46. Sobral, A.; Vacavant, A. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput. Vis. Image Underst. 2014, 122, 4-21.
47. Kaewtrakulpong, P.; Bowden, R. An improved adaptive background mixture model for realtime tracking with shadow detection. In Video-Based Surveillance Systems; Springer: New York, NY, USA, 2001.
48. Noh, S.J.; Jeon, M. A New Framework for Background Subtraction Using Multiple Cues. In Proceedings of the 11th Asian Conference on Computer Vision (ACCV), Firenze, Italy, 7-13 October 2012; pp. 493-506.
49. Horn, B.K.P. Robot Vision; MIT Press: Cambridge MA, United States, 1986.
50. Sobral, A. BGSLibrary: An OpenCV C++ Background Subtraction Library. In Proceedings of the IX Workshop de Visão Computacional (WVC'2013), Rio de Janeiro, Brazil, 3-5 June 2013.
51. Padilla-López, J.R.; Flórez-Revuelta, F.; Monekosso, D.N.; Remagnino, P. The "Good" Brother: Monitoring People Activity in Private Spaces. In Distributed Computing and Artificial Intelligence; Advances in Intelligent and Soft Computing; Springer: Berlin/Heidelberg, Germany, 2012; Volume 151, pp. 49-56.