Abstract
Home automation poses requirements, which are typically solved by AI methods. The paper focuses on the problem of protecting video-surveillance systems against tampering actions, and proposes a new algorithm. This is based on a model of the environment observed by the camera, which must be protected. The model is automatically learned by observing the video stream generated by the camera. The method is now implemented in a commercial system are the results reported from seven experimental sites shows an excellent performance outperforming state of the art algorithms described in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cook, D.J., Augusto, J., Jakkula, V.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing 5(4), 277–299 (2009)
Simpson, R., Schreckenghost, D., LoPresti, E.F., Kirsch, N.: Plans and Planning in Smart Homes. In: Augusto, J.C., Nugent, C.D. (eds.) Designing Smart Homes. LNCS (LNAI), vol. 4008, pp. 71–84. Springer, Heidelberg (2006)
Ramos, C.: Ambient Intelligence – A State of the Art from Artificial Intelligence Perspective. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 285–295. Springer, Heidelberg (2007)
Mendola, D., Monfrecola, A., Moio, A., Giordana, A.: Horus: an agent based system for home automation. Technical report TR-01-2012 (2012)
Saglam, A.: Adaptive camera tamper detection for video surveillance (June 2009)
Ribnick, E., Atev, S., Masoud, O., Papanikolopoulos, N., Voyles, R.: Real-time detection of camera tampering. In: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, AVSS 2006, p. 10. IEEE Computer Society, Washington, DC (2006)
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Djouadi, A., Snorrason, O., Garber, F.: The quality of training-sample estimates of the bhattacharyya coefficient. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 92–97 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moio, A., Giordana, A., Mendola, D. (2012). An Anti-tampering Algorithm Based on an Artificial Intelligence Approach. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-34624-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34623-1
Online ISBN: 978-3-642-34624-8
eBook Packages: Computer ScienceComputer Science (R0)