Abstract
Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality, many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.
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References
Bacchus, F., Halpern, J., Levesque, H.: Reasoning about noisy sensors and effectors in the situation calculus. Artificial Intelligence 111(1-2), 171–208 (1998)
Bsia: Florida school bus surveillance, http://www.bsia.co.uk/LY8VIM18989_action;displaystudy_sectorid;LYCQYL79312_caseid;NFLEN064798
De Giacomo, G., Levesque, H.: Progression using regression and sensors. In: Procs. of IJCAI, pp. 160–165 (1999)
Herzig, A., Lang, J., Marquis, P.: Action representation and partially observable planning using epistemic logic. In: Procs. of IJCAI, pp. 1067–1072 (2003)
Lakemeyer, G., Levesque, H.: A semantic characterization of a useful fragment of the situation calculus with knowledge. Artificial Intelligence 175(1), 142–164 (2011)
Levesque, H.: What is planning in the presence of sensing. In: Procs. of AAAI, pp. 1139–1146 (1996)
Liu, W., Miller, P., Ma, J., Yan, W.: Challenges of distributed intelligent surveillance system with heterogenous information. In: Procs. of QRASA, Pasadena, California, pp. 69–74 (2009)
Ma, J., Liu, W., Benferhat, S.: A belief revision framework for revising epistemic states with partial epistemic states. In: Procs. of AAAI, pp. 333–338 (2010)
Ma, J., Liu, W., Hunter, A.: Inducing probability distributions from knowledge bases with (in)dependence relations. In: Procs. of AAAI, pp. 339–344 (2010)
Ma, J., Liu, W., Miller, P.: Event modelling and reasoning with uncertain information for distributed sensor networks. In: Deshpande, A., Hunter, A. (eds.) SUM 2010. LNCS, vol. 6379, pp. 236–249. Springer, Heidelberg (2010)
Ma, J., Liu, W., Miller, P.: Belief change with noisy sensing in the situation calculus. In: Procs. of UAI (2011)
Ma, J., Liu, W., Miller, P., Yan, W.: Event composition with imperfect information for bus surveillance. In: Procs. of AVSS, pp. 382–387. IEEE Press, Los Alamitos (2009)
McCarthy, J.: Situations, Actions and Causal Laws. Stanford University, Stanford (1963)
McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. In: Machine Intelligence, vol. 4, pp. 463–502. Edinburgh University Press, Edinburgh (1969)
Miller, P., Liu, W., Fowler, F., Zhou, H., Shen, J., Ma, J., Zhang, J., Yan, W., McLaughlin, K., Sezer, S.: Intelligent sensor information system for public transport: To safely go.. In: Procs. of AVSS (2010)
ECIT Queen’s University of Belfast. Airport corridor surveillance (2010), http://www.csit.qub.ac.uk/Research/ResearchGroups/IntelligentSurveillanceSystems
US Defense of the Homeland. Washington rail corridor surveillance (2006), http://preview.govtsecurity.com/news/Washington-rail-corridor-surveillance/
US Department of Transportation. Rita - its research program (2010), http://www.its.dot.gov/ITS_ROOT2010/its_program/ITSfederal_program.htm
Reiter, R.: The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression. In: Aritificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, pp. 359–380. Academic Press, London (1991)
Scherl, R., Levesque, H.: The frame problem and knowledge-producing actions. In: Procs. of AAAI, pp. 689–695 (1993)
Scherl, R., Levesque, H.: Knowledge, action, and the frame problem. Artificial Intelligence 144(1-2), 1–39 (2003)
Gardiner Security. Glasgow transforms bus security with ip video surveillance, http://www.ipusergroup.com/doc-upload/Gardiner-Glasgowbuses.pdf
Shapiro, S.: Belief change with noisy sensing and introspection. In: Procs. of NRAC, pp. 84–89 (2005)
Shapiro, S., Pagnucco, M., Lespérance, Y., Levesque, H.: Iterated belief change in the situation calculus. Artificial Intelligence 175(1), 165–192 (2011)
Shet, V.D., Neumann, J., Ramesh, V., Davis, L.S.: Bilattice-based logical reasoning for human detection. In: IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)
Wang, T., Diao, Q., Zhang, Y., Song, G., Lai, C., Bradski, G.: A dynamic bayesian network approach to multi-cue based visual tracking. In: Procs. of Pattern Recognition, ICPR, pp. 167–170 (2004)
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Ma, J., Liu, W., Miller, P. (2011). Handling Sequential Observations in Intelligent Surveillance. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_43
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DOI: https://doi.org/10.1007/978-3-642-23963-2_43
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