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

Youn Suk Song; Sung Bae Cho; Il Hong Suh

doi:10.1007/11553939_132

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

Youn Suk Song, Sung Bae Cho, Il Hong Suh

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 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 language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Publisher	Springer Verlag
Pages	937-944
Number of pages	8
ISBN (Print)	3540288961, 9783540288961
DOIs	https://doi.org/10.1007/11553939_132
Publication status	Published - 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 2005 Sept 14 → 2005 Sept 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3683 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	05/9/14 → 05/9/16

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11553939_132

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). Springer Verlag. https://doi.org/10.1007/11553939_132

Song, Youn Suk ; Cho, Sung Bae ; Suh, Il Hong. / Activity-object bayesian networks for detecting occluded objects in uncertain indoor environment. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. pp. 937-944 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Activity-object bayesian networks for detecting occluded objects in uncertain indoor environment",

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.",

author = "Song, {Youn Suk} and Cho, {Sung Bae} and Suh, {Il Hong}",

year = "2005",

doi = "10.1007/11553939_132",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings",

address = "Germany",

note = "9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 ; Conference date: 14-09-2005 Through 16-09-2005",

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Song, YS, Cho, SB & Suh, IH 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, Springer Verlag, pp. 937-944, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 05/9/14. https://doi.org/10.1007/11553939_132

Activity-object bayesian networks for detecting occluded objects in uncertain indoor environment. / Song, Youn Suk; Cho, Sung Bae; Suh, Il Hong.
Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. p. 937-944 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Cho, Sung Bae

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N2 - 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.

AB - 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.

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Song YS, Cho SB, Suh IH. 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. Springer Verlag. 2005. p. 937-944. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11553939_132

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

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