Activity-object bayesian networks for detecting occluded objects in uncertain indoor environment

Youn Suk Song, Sung Bae Cho, Il Hong Suh

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

5 Citations (Scopus)

Abstract

In the field of the service robots, object detection and scene understanding are very important. Conventional methods for object detection are performed with the geometric models, but they have limitations to be used in the uncertain and dynamic environments. This paper proposes a method to predict the probability of target object with Bayesian networks modeled based on activity-object relations. Experiments in indoor office environment show the usefulness of the proposed method for object detection, which produces about 86.5% of accuracy with environments.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Pages937-944
Number of pages8
Publication statusPublished - 2005 Dec 1
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sep 142005 Sep 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3683 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
CountryAustralia
CityMelbourne
Period05/9/1405/9/16

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Song, Y. S., Cho, S. B., & Suh, I. H. (2005). Activity-object bayesian networks for detecting occluded objects in uncertain indoor environment. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 937-944). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).