Inferring border crossing intentions with hidden Markov models

Gurmeet Singh, Kishan G. Mehrotra, Chilukuri K. Mohan, Thyagaraju Damarla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationModern Approaches in Applied Intelligence - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Proceedings
Pages69-78
Number of pages10
EditionPART 1
DOIs
StatePublished - Jul 25 2011
Event24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011 - Syracuse, NY, United States
Duration: Jun 28 2011Jul 1 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6703 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011
CountryUnited States
CitySyracuse, NY
Period6/28/117/1/11

Keywords

  • Border Surveillance
  • Hidden Markov Models
  • Intention Modeling
  • Soft and Hard Data Fusion

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Singh, G., Mehrotra, K. G., Mohan, C. K., & Damarla, T. (2011). Inferring border crossing intentions with hidden Markov models. In Modern Approaches in Applied Intelligence - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Proceedings (PART 1 ed., pp. 69-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6703 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-21822-4_8