Abstract
Law enforcement officials are confronted with the difficult task of monitoring large stretches of international borders in order to prevent illegal border crossings.Sensor technologies are currently in use to assist this task, and the availability of additional human intelligence reports can improve monitoring performance. This paper attempts to use human observations of subjects’ behaviors (prior to border crossing) in order to make tentative inferences regarding their intentions to cross. We develop a Hidden Markov Model (HMM) approach to model border crossing intentions and their relation to physical observations, and show that HMM parameters can be learnt using location data obtained from samples of simulated physical paths of subjects. We use a probabilistic approach to fuse “soft” data (human observation reports) with “hard” (sensor) data. Additionally, HMM simulations are used to predict the probability with which crossings by these subjects might occur at different locations.
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References
Brdiczka, O., Langet, M., Maisonnasse, J., Crowley, J.L.: Detecting Human Behavior Models From Multimodal Observation in a Smart Home. IEEE Transactions on Automation Science and Engineering
Feldman, A., Balch, T.: Representing Honey Bee Behavior for Recognition Using Human Trainable Models. Adapt. Behav. 12, 241–250 (2004)
Hall, D.L., Llinas, J., Neese, M.M., Mullen, T.: A Framework for Dynamic Hard/Soft Fusion. In: Proceedings of the 11th International Conferenec on Information Fusion (2008)
Brooks, R., Iyengar, S.: Multi-Sensor Fusion: Fundamentals and Applications with Software. Prentice Hall, Englewood Cliffs (1997)
Jones, R.E.T., Connors, E.S., Endsley, M.R.: Incorporating the Human Analyst into the Data Fusion Process by Modeling Situation Awareness Using Fuzzy Cognitive Maps. In: 12th International Conference on Information Fusion, Seattle, WA, USA, July 6-9 (2009)
Kelley, R., Tavakkoli, A., King, C., Nicolescu, M., Nicolescu, M., Bebis, G.: Understanding Human Intentions via Hidden Markov Models in Autonomous Mobile Robots. In: HRI 2008, Amsterdam, Netherlands (2008)
Laudy, C., Goujon, B.: Soft Data Analysis within a Decision Support System. In: 12th International Conference on Information Fusion, Seattle, WA, USA, July 6-9 (2009)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2) (February 1989)
Rickard, J.T.: Level 2/3 Fusion in Conceptual Spaces. In: 10th International Conference on Information Fusion, Quebeck, Canada, July 9-12 (2007)
Sambhoos, K., Llinas, J., Little, E.: Graphical Methods for Real-Time Fusion and Estimation with Soft Message Data. In: 11th International Conference on Information Fusion, Cologne, Germany, June 30-July 3 (2008)
Youn, S.-J., Oh, K.-W.: Intention Recognition using a Graph Representation. World Academy of Science, Engineering and Technology 25 (2007)
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© 2011 Springer-Verlag Berlin Heidelberg
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Singh, G., Mehrotra, K.G., Mohan, C.K., Damarla, T. (2011). Inferring Border Crossing Intentions with Hidden Markov Models. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_8
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DOI: https://doi.org/10.1007/978-3-642-21822-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
Online ISBN: 978-3-642-21822-4
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