Commenced in January 2007
Paper Count: 30172
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060379Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1315
 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.
 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.
 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.
 M. Roman and R. H. Campbell. Gaia: Enabling active spaces. In Proceedings of the 9th ACM SIGOPS European Workshop, Kolding, 2000.
 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.
 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.
 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.
 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.
 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.
 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.
 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)
 D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60:91-110, 2004.
 P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. pages 511-518, 2001.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 S. Ayache and G. Quenot. Trecvid 2007 collaborative annotation using active learning. In In Proceedings of the TRECVID 2007 Workshop, 2007.
 G. Heidemann, A. Saalbach, and H. Ritter. Semi-automatic acquisition and labeling of image data using SOMs. In ESANN, pages 503-508, 2003.
 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.
 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.
 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.
 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)
 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.
 Unabhaengiges Landeszentrum fuer Datenschutz Schleswig-Holstein. Videoueberwachung und Webkameras (German). Blaue Reihe Vol. 4. URL=https://www.datenschutzzentrum.de/blauereihe/blauereihevideo. pdf
 M. Lang. Private Videoueberwachung im oeffentlichen Raum. Eine Untersuchung der Zulaessigkeit des privaten Einsatzes von Videotechnik und der Notwendigkeit von ┬º 6b BDSG als spezielle rechtliche Regelung (German). Hamburg, 2008.
 A. Rossnagel, S. Jandt, J. Mueller, A. Gutscher, and J. Heesen. Datenschutzfragen mobiler kontextbezogener Systeme (German). Wiesbaden, 2006.
 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.
 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)