Context Generation with Image Based Sensors: An Interdisciplinary Enquiry on Technical and Social Issues and their Implications for System Design
Image data holds a large amount of different context
information. However, as of today, these resources remain largely
untouched. It is thus the aim of this paper to present a basic technical
framework which allows for a quick and easy exploitation of context
information from image data especially by non-expert users.
Furthermore, the proposed framework is discussed in detail
concerning important social and ethical issues which demand special
requirements in system design. Finally, a first sensor prototype is
presented which meets the identified requirements. Additionally,
necessary implications for the software and hardware design of the
system are discussed, rendering a sensor system which could be
regarded as a good, acceptable and justifiable technical and thereby
enabling the extraction of context information from image data.
[1] A. K. Dey, G. D. Abowd, P. J. Brown, N. Davies, M. Smith, and P.
Steggles. Towards a better understanding of context and
contextawareness. In Proceedings of the 1st International Symposium on
Handheld and Ubiquitous Computing, 1999.
[2] M. Großmann, M. Bauer, N. Hoenle, U.-P. Kaeppeler, D. Nicklas, and T.
Schwarz. Efficiently managing context information for large-scale
scenarios. In Proceedings of the 3rd IEEE International Conference on
Pervasive Computing and Communications, 2005.
[3] K. Henricksen and J. Indulska. A software engineering framework for
context-aware pervasive computing. In Proceedings of the 2nd IEEE
International Conference on Pervasive Computing and Communications,
2004.
[4] M. Roman and R. H. Campbell. Gaia: Enabling active spaces. In
Proceedings of the 9th ACM SIGOPS European Workshop, Kolding,
2000.
[5] D. Salber, A., K. Dey, and G. D. Abowd. The Context Toolkit: Aiding
the development of context-enabled applications. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems: The CHI
is the Limit, 1999.
[6] A. Blessing, A.; S. Klatt, D. Nicklas, S. Volz, and H. Schuetze.
Language-Derived Information and Context Models. In Proceedings of
the 3rd Workshop on Context Modelling and Reasoning at 4th IEEE
International Conference on Pervasive Computing and Communication
perCom, 2006.
[7] U.-P. Kaeppeler, R. Benkmann, O. Zweigle, R. Lafrenz, P. Levi:
Resolving Inconsistencies in Shared Context Models using Multiagent
Systems. In R. Dillmann and W. Burgard (eds.): Proceedings of the 10th
International Conference on Intelligent Autonomous Systems: IAS-10;
Baden Baden, Germany, July 23-25, 2008.
[8] M. Bauer, L. Jendoubi, and O. Siemoneit. Smart Factory - Mobile
Computing in Production Environments. In Proceedings of the MobiSys
Workshop on Applications of Mobile Embedded Systems WAMES,
2004.
[9] L. M. Hilty, C. Som, and A. Koehler. Impacts of Future Information and
Communication Technologies on Society and Environment. In G. Banse,
I. Hronszky, and G. Nelson (eds.): Rationality in an uncertain world.
edition sigma, 2005, 205-290.
[10] M. Bauer, C. Becker, J. Haehner, and G. Schiele. ContextCube -
Providing context information ubiquitously. In Proceedings of the 3rd
International Workshop on Smart Appliances and Wearable Computing,
2003.
[11] U.-P. Kaeppeler, A. Gerhardt, C. Schieberle, M. Wiselka, K.
Haeussermann, O. Zweigle, and P. Levi. Reliable situation recognition
based on noise levels. In Proceedings of the 1st International Conference
on Disaster Management and Human Health Risk, 2009. (Forthcoming)
[12] D. G. Lowe. Distinctive image features from scale-invariant keypoints.
International Journal of Computer Vision, 60:91-110, 2004.
[13] P. Viola and M. Jones. Rapid object detection using a boosted cascade of
simple features. pages 511-518, 2001.
[14] H. Bay, A. Ess, T. Tuytelaars, and L. van Gool. Speeded-up robust
features (surf) ). Computer Vision and Image Understanding, 110(3):346
- 359, 2008. Similarity Matching in Computer Vision and Multimedia.
[15] J.Kittler, M. Hatef, R. P.W. Duin, and J. Matas. On combining
classifiers. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 20(3):226-239, 1998.
[16] Z. Stejic, Y. Takama, and K. Hirota. Mathematical aggregation operators
in image retrieval: effect on retrieval performance and role in relevance
feedback. Signal Processing, 85(2):297 - 324, 2005. SI on Content
Based Image and Video Retrieval.
[17] M. Varma and D. Ray. Learning the discriminative power-invariance
trade-off. In Computer Vision, 2007. ICCV 2007. IEEE 11th
International Conference on, pages 1-8, Oct. 2007.
[18] O. R. Terrades, E. Valveny, and S.Tabbone. Optimal classifier fusion in
a non-bayesian probabilistic framework. IEEE Trans. Pattern Anal.
Mach. Intell., 31(9):1630-1644, 2009.
[19] A. Opelt, A. Pinz, M. Fussenegger, and P. Auer. Generic object
recognition with boosting. Pattern Analysis and Machine Intelligence,
IEEE Transactions on, 28(3):416-431, March 2006.
[20] B.C. Russell, A. Torralba, K.P. Murphy, and W.T. Freeman. LabelMe: A
Database and Web-based Tool for Image Annotation. Int. J. Comput.
Vision, 77(1-3):157-173, 2008.
[21] L. von Ahn and L. Dabbish. Labeling images with a computer game. In
Human factors in computing systems. CHI 04. SIGHI conference on.
Pages 319-326, 2004.
[22] S. Ayache and G. Quenot. Trecvid 2007 collaborative annotation using
active learning. In In Proceedings of the TRECVID 2007 Workshop,
2007.
[23] G. Heidemann, A. Saalbach, and H. Ritter. Semi-automatic acquisition
and labeling of image data using SOMs. In ESANN, pages 503-508,
2003.
[24] H. Bekel, G. Heidemann, and H. Ritter. Interactive image data labeling
using self-organizing maps in an augmented reality scenario. Neural
Netw., 18(5-6):566-574, 2005.
[25] T. Schreck, J. Bernard, T. von Landesberger, and J. Kohlhammer. Visual
cluster analysis of trajectory data with interactive kohonen maps.
Information Visualization, 8(1):14-29.
[26] D. Lucke, E. Westkaemper, M. Eissele, T. Ertl, O. Siemoneit, and C.
Hubig. Privacy-Preserving Self-Localization Techniques in Next
Generation Manufacturing. An Interdisciplinary View on the Vision and
Implementation of Smart Factories. In Proceeding of the 10th
International Conference on Control, Automation, Robotics and Vision
ICARCV, 2008.
[27] M. Wieland,C. Laengerer, F. Leymann, O. Siemoneit, and C. Hubig.
Methods for Conserving Privacy in Workflow Controlled Smart
Environments. A Technical and Philosophical Enquiry into Human-
Oriented System Design of Ubiquitous Work Environments. In
Proceedings of the 3rd International Conference on Mobile Ubiquitous
Computing, Systems, Services and Technologies Ubicomm, 2009.
(Forthcoming)
[28] F. Porikli. Trajectory distance metric using hidden markov model based
representation. In Proceedings of the 8th IEEE European Conference on
Computer Vision, PETS Workshop, 2004.
[29] Unabhaengiges Landeszentrum fuer Datenschutz Schleswig-Holstein.
Videoueberwachung und Webkameras (German). Blaue Reihe Vol. 4.
URL=https://www.datenschutzzentrum.de/blauereihe/blauereihevideo.
pdf
[30] M. Lang. Private Videoueberwachung im oeffentlichen Raum. Eine
Untersuchung der Zulaessigkeit des privaten Einsatzes von Videotechnik
und der Notwendigkeit von § 6b BDSG als spezielle rechtliche Regelung
(German). Hamburg, 2008.
[31] A. Rossnagel, S. Jandt, J. Mueller, A. Gutscher, and J. Heesen.
Datenschutzfragen mobiler kontextbezogener Systeme (German).
Wiesbaden, 2006.
[32] O. Siemoneit, C. Hubig, M. Kada, M. Peter, and D. Fritsch. Google
Street View and Privacy. Some thoughts from a philosophical and
engineering point of view. In Proceedings of the 5th Asiac-Pacific
Conference on Computing and Philosophy, 2009.
[33] M. Kada, M. Peter, D. Fritsch, O. Siemoneit, and C. Hubig. Privacy-
Enabling Abstraction and Obfuscation Techniques for 3D City Models.
In Proceeding of the 2nd SIGSPATIAL ACM GIS International
Workshop on Security and Privacy in GIS and LBS, 2009.
(Forthcoming)
[1] A. K. Dey, G. D. Abowd, P. J. Brown, N. Davies, M. Smith, and P.
Steggles. Towards a better understanding of context and
contextawareness. In Proceedings of the 1st International Symposium on
Handheld and Ubiquitous Computing, 1999.
[2] M. Großmann, M. Bauer, N. Hoenle, U.-P. Kaeppeler, D. Nicklas, and T.
Schwarz. Efficiently managing context information for large-scale
scenarios. In Proceedings of the 3rd IEEE International Conference on
Pervasive Computing and Communications, 2005.
[3] K. Henricksen and J. Indulska. A software engineering framework for
context-aware pervasive computing. In Proceedings of the 2nd IEEE
International Conference on Pervasive Computing and Communications,
2004.
[4] M. Roman and R. H. Campbell. Gaia: Enabling active spaces. In
Proceedings of the 9th ACM SIGOPS European Workshop, Kolding,
2000.
[5] D. Salber, A., K. Dey, and G. D. Abowd. The Context Toolkit: Aiding
the development of context-enabled applications. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems: The CHI
is the Limit, 1999.
[6] A. Blessing, A.; S. Klatt, D. Nicklas, S. Volz, and H. Schuetze.
Language-Derived Information and Context Models. In Proceedings of
the 3rd Workshop on Context Modelling and Reasoning at 4th IEEE
International Conference on Pervasive Computing and Communication
perCom, 2006.
[7] U.-P. Kaeppeler, R. Benkmann, O. Zweigle, R. Lafrenz, P. Levi:
Resolving Inconsistencies in Shared Context Models using Multiagent
Systems. In R. Dillmann and W. Burgard (eds.): Proceedings of the 10th
International Conference on Intelligent Autonomous Systems: IAS-10;
Baden Baden, Germany, July 23-25, 2008.
[8] M. Bauer, L. Jendoubi, and O. Siemoneit. Smart Factory - Mobile
Computing in Production Environments. In Proceedings of the MobiSys
Workshop on Applications of Mobile Embedded Systems WAMES,
2004.
[9] L. M. Hilty, C. Som, and A. Koehler. Impacts of Future Information and
Communication Technologies on Society and Environment. In G. Banse,
I. Hronszky, and G. Nelson (eds.): Rationality in an uncertain world.
edition sigma, 2005, 205-290.
[10] M. Bauer, C. Becker, J. Haehner, and G. Schiele. ContextCube -
Providing context information ubiquitously. In Proceedings of the 3rd
International Workshop on Smart Appliances and Wearable Computing,
2003.
[11] U.-P. Kaeppeler, A. Gerhardt, C. Schieberle, M. Wiselka, K.
Haeussermann, O. Zweigle, and P. Levi. Reliable situation recognition
based on noise levels. In Proceedings of the 1st International Conference
on Disaster Management and Human Health Risk, 2009. (Forthcoming)
[12] D. G. Lowe. Distinctive image features from scale-invariant keypoints.
International Journal of Computer Vision, 60:91-110, 2004.
[13] P. Viola and M. Jones. Rapid object detection using a boosted cascade of
simple features. pages 511-518, 2001.
[14] H. Bay, A. Ess, T. Tuytelaars, and L. van Gool. Speeded-up robust
features (surf) ). Computer Vision and Image Understanding, 110(3):346
- 359, 2008. Similarity Matching in Computer Vision and Multimedia.
[15] J.Kittler, M. Hatef, R. P.W. Duin, and J. Matas. On combining
classifiers. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 20(3):226-239, 1998.
[16] Z. Stejic, Y. Takama, and K. Hirota. Mathematical aggregation operators
in image retrieval: effect on retrieval performance and role in relevance
feedback. Signal Processing, 85(2):297 - 324, 2005. SI on Content
Based Image and Video Retrieval.
[17] M. Varma and D. Ray. Learning the discriminative power-invariance
trade-off. In Computer Vision, 2007. ICCV 2007. IEEE 11th
International Conference on, pages 1-8, Oct. 2007.
[18] O. R. Terrades, E. Valveny, and S.Tabbone. Optimal classifier fusion in
a non-bayesian probabilistic framework. IEEE Trans. Pattern Anal.
Mach. Intell., 31(9):1630-1644, 2009.
[19] A. Opelt, A. Pinz, M. Fussenegger, and P. Auer. Generic object
recognition with boosting. Pattern Analysis and Machine Intelligence,
IEEE Transactions on, 28(3):416-431, March 2006.
[20] B.C. Russell, A. Torralba, K.P. Murphy, and W.T. Freeman. LabelMe: A
Database and Web-based Tool for Image Annotation. Int. J. Comput.
Vision, 77(1-3):157-173, 2008.
[21] L. von Ahn and L. Dabbish. Labeling images with a computer game. In
Human factors in computing systems. CHI 04. SIGHI conference on.
Pages 319-326, 2004.
[22] S. Ayache and G. Quenot. Trecvid 2007 collaborative annotation using
active learning. In In Proceedings of the TRECVID 2007 Workshop,
2007.
[23] G. Heidemann, A. Saalbach, and H. Ritter. Semi-automatic acquisition
and labeling of image data using SOMs. In ESANN, pages 503-508,
2003.
[24] H. Bekel, G. Heidemann, and H. Ritter. Interactive image data labeling
using self-organizing maps in an augmented reality scenario. Neural
Netw., 18(5-6):566-574, 2005.
[25] T. Schreck, J. Bernard, T. von Landesberger, and J. Kohlhammer. Visual
cluster analysis of trajectory data with interactive kohonen maps.
Information Visualization, 8(1):14-29.
[26] D. Lucke, E. Westkaemper, M. Eissele, T. Ertl, O. Siemoneit, and C.
Hubig. Privacy-Preserving Self-Localization Techniques in Next
Generation Manufacturing. An Interdisciplinary View on the Vision and
Implementation of Smart Factories. In Proceeding of the 10th
International Conference on Control, Automation, Robotics and Vision
ICARCV, 2008.
[27] M. Wieland,C. Laengerer, F. Leymann, O. Siemoneit, and C. Hubig.
Methods for Conserving Privacy in Workflow Controlled Smart
Environments. A Technical and Philosophical Enquiry into Human-
Oriented System Design of Ubiquitous Work Environments. In
Proceedings of the 3rd International Conference on Mobile Ubiquitous
Computing, Systems, Services and Technologies Ubicomm, 2009.
(Forthcoming)
[28] F. Porikli. Trajectory distance metric using hidden markov model based
representation. In Proceedings of the 8th IEEE European Conference on
Computer Vision, PETS Workshop, 2004.
[29] Unabhaengiges Landeszentrum fuer Datenschutz Schleswig-Holstein.
Videoueberwachung und Webkameras (German). Blaue Reihe Vol. 4.
URL=https://www.datenschutzzentrum.de/blauereihe/blauereihevideo.
pdf
[30] M. Lang. Private Videoueberwachung im oeffentlichen Raum. Eine
Untersuchung der Zulaessigkeit des privaten Einsatzes von Videotechnik
und der Notwendigkeit von § 6b BDSG als spezielle rechtliche Regelung
(German). Hamburg, 2008.
[31] A. Rossnagel, S. Jandt, J. Mueller, A. Gutscher, and J. Heesen.
Datenschutzfragen mobiler kontextbezogener Systeme (German).
Wiesbaden, 2006.
[32] O. Siemoneit, C. Hubig, M. Kada, M. Peter, and D. Fritsch. Google
Street View and Privacy. Some thoughts from a philosophical and
engineering point of view. In Proceedings of the 5th Asiac-Pacific
Conference on Computing and Philosophy, 2009.
[33] M. Kada, M. Peter, D. Fritsch, O. Siemoneit, and C. Hubig. Privacy-
Enabling Abstraction and Obfuscation Techniques for 3D City Models.
In Proceeding of the 2nd SIGSPATIAL ACM GIS International
Workshop on Security and Privacy in GIS and LBS, 2009.
(Forthcoming)
@article{"International Journal of Information, Control and Computer Sciences:54170", author = "Julia Moehrmann and Gunter Heidemann and Oliver Siemoneit and Christoph Hubig and Uwe-Philipp Kaeppeler and Paul Levi", title = "Context Generation with Image Based Sensors: An Interdisciplinary Enquiry on Technical and Social Issues and their Implications for System Design", abstract = "Image data holds a large amount of different context
information. However, as of today, these resources remain largely
untouched. It is thus the aim of this paper to present a basic technical
framework which allows for a quick and easy exploitation of context
information from image data especially by non-expert users.
Furthermore, the proposed framework is discussed in detail
concerning important social and ethical issues which demand special
requirements in system design. Finally, a first sensor prototype is
presented which meets the identified requirements. Additionally,
necessary implications for the software and hardware design of the
system are discussed, rendering a sensor system which could be
regarded as a good, acceptable and justifiable technical and thereby
enabling the extraction of context information from image data.", keywords = "Context-aware computing, ethical and social issues, image recognition, requirements in system design.", volume = "4", number = "1", pages = "81-7", }
