SmaCCS: Smart Camera Cloud Services - Towards an Intelligent Cloud-based Surveillance System

Sven Tomforde, Uwe Jänen, Jörg Hähner, Martin Hoffmann

2013

Abstract

Today, high performance and feature rich surveillance systems are very costly as they require an expensive set of infrastructure components. As a consequence, such systems including, e.g., complex automatic video content analysis, are restricted to large scale applications, such as airports or train stations. In smaller settings, e.g. in shop surveillance, mostly low-cost display or record-only systems are in use. In this position paper we propose to combine two well-known approaches in order to make Intelligent Video Surveillance applicable and affordable in small to medium-scale scenarios. The proposal includes to combine the concept of Smart Cameras, i.e. cameras equipped with local processing resources, with the ideas of Cloud Computing, i.e. the on-demand provisioning of computing and storage services for complex calculations, and the management of large amounts of data, i.e. video storage. The former allows for the cost effective pre-processing of video data close to the sensor, while using the latter concept does not require large initial investments into expensive infrastructure components such as powerful compute servers. The paper presents research issues of the necessary system design, including precise system goal and system model aspects. Based on this, we discuss several research issues required to be addressed for solving the overall goals.

References

  1. Bryant, R. E., Katz, R. H., and Lazowska, E. D. (2008). Big-data computing: Creating revolutionary breakthroughs in commerce, science, and society. Computing Research Initiatives for the 21st Century. Computing Research Association.
  2. Cernium (2013). Perceptrac System webpage. Online, http://www.cernium.com.
  3. Chu, M., Reich, J., and Zhao, F. (2004). Distributed attention in large scale video sensor networks. IEE Seminar Digests, 2004:61-65.
  4. Collins, R. T., Lipton, A. J., Fujiyoshi, H., and Kanade, T. (2001). Algorithms for cooperative multisensor surveillance. In Surveillance, Proceedings of the IEEE.
  5. D'Angelo, D., Grenz, C., Kuntzsch, C., and Bogen, M. (2012). CamInSens - An Intelligent in-situ Security System for Public Spaces. In Proceedings of the 2012 International Conference on Security & Management (SAM), 2012, 16.-19. Jul, Las Vegas, USA. CSREA Press.
  6. Hoffmann, M., Jänen, U., Fares, A., and Hähner, J. (2010). Amidivin: basic algorithms for alarm management in distributed vision networks. In Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 7810, pages 150-157, New York, NY, USA. ACM.
  7. Hornung, G. and Desoi, M. (2011). Smart Cameras und automatische Verhaltensanalyse. Kommunikation und Recht, pages 153-158.
  8. Jänen, U., Feuerhake, U., Klinger, T., Muhle, D., Hähner, J., Sester, M., and Heipke, C. (2012). QTrajectories: Improving the Quality of Object Tracking Using SelfOrganizing Camera Networks. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, pages 269 - 274.
  9. Javed, O. and Shah, M. (2008). Automated Multi-Camera Surveillance: Algorithms and Practice. Springer Publishing Company, Incorporated, 1 edition.
  10. Kapadia, A., Myers, S., Wang, X., and Fox, G. C. (2010). Secure cloud computing with brokered trusted sensor networks. In Collaborative Technologies and Systems (CTS), 2010 International Symposium on, pages 581- 592.
  11. Lipton, A. J., Clark, J. I., Brewer, P., Venetianer, P. L., and Chosak, A. J. (2004). Objectvideo forensics: activitybased video indexing and retrieval for physical security applications. In Intelligent Distributed Surveilliance Systems, IEEE, pages 56-60.
  12. Monari, E. (2012). Dynamische Sensorselektion zur auftragsbasierten Objektverfolgung in Kameranetzwerken. KIT Scientific Publishing. ISBN: ISBN 978- 3866447295.
  13. Nagios Enterprises (2013). http://www.nagios.org/.
  14. Pearson, S. (2009). Taking account of privacy when designing cloud computing services. In Software Engineering Challenges of Cloud Computing, 2009. CLOUD 7809. ICSE Workshop on, pages 44-52.
  15. Schneiderman, R. (1975). Smart cameras clicking with electronic functions. Electronics, pages 74 - 81.
  16. SML (2013). Sensor Model Language webpage. Online, http://www.opengeospatial.org.
  17. Tomforde, S. (2012). Runtime adaptation of technical systems: An architectural framework for self-configuration and self-improvement at runtime. Südwestdeutscher Verlag für Hochschulschriften. ISBN: 978-3838131337.
  18. Tomforde, S., Hurling, B., and Hähner, J. (2011). Distributed Network Protocol Parameter Adaptation in Mobile Ad-Hoc Networks. In Informatics in Control, Automation and Robotics, volume 89 of LNEE, pages 91 - 104. Springer.
  19. Vaquero, L. M., Rodero-Merino, L., Caceres, J., and Lindner, M. (2008). A break in the clouds: towards a cloud definition. SIGCOMM Comput. Commun. Rev., 39(1):50-55.
  20. Velastin, S. and Remagnino, P. (2006). Intelligent Distributed Video Surveillance Systems. Professional Applications of Computing. Institution of Engineering and Technology. ISBN: 978-0863415043.
  21. Wen, Y., Yang, X., and Xu, Y. (2010). Cloud-computingbased framework for multi-camera topology inference in smart city sensing system. In Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing, MCMC 7810, pages 65-70, New York, NY, USA. ACM.
  22. Yee, C. K., Ling, C. S., Yee, W. S., and Zainon, W. (2012). Gui design based on cognitive psychology: Theoretical, empirical and practical approaches. In Computing Technology and Information Management (ICCM), 2012 8th International Conference on, volume 2, pages 836-841.
  23. Zhang, L., Malki, S., and Spaanenburg, L. (2009). Intelligent camera cloud computing. In Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on, pages 1209-1212.
  24. Zick, T. (2007). Clouds, Cameras, and Computers: The First Amendment and Networked Public Places. Florida Law Review, 69(St. John's Legal Studies Research Paper No. 06-0062):1 - 66. Available at SSRN: http://ssrn.com/abstract=956160.
Download


Paper Citation


in Harvard Style

Tomforde S., Jänen U., Hähner J. and Hoffmann M. (2013). SmaCCS: Smart Camera Cloud Services - Towards an Intelligent Cloud-based Surveillance System . In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-70-9, pages 288-293. DOI: 10.5220/0004590902880293


in Bibtex Style

@conference{icinco13,
author={Sven Tomforde and Uwe Jänen and Jörg Hähner and Martin Hoffmann},
title={SmaCCS: Smart Camera Cloud Services - Towards an Intelligent Cloud-based Surveillance System},
booktitle={Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2013},
pages={288-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004590902880293},
isbn={978-989-8565-70-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - SmaCCS: Smart Camera Cloud Services - Towards an Intelligent Cloud-based Surveillance System
SN - 978-989-8565-70-9
AU - Tomforde S.
AU - Jänen U.
AU - Hähner J.
AU - Hoffmann M.
PY - 2013
SP - 288
EP - 293
DO - 10.5220/0004590902880293