Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

A detection method based on Bayesian hierarchical network for abnormal interaction

Authors
Ye Su, JianXin Song
Corresponding Author
Ye Su
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.66How to use a DOI?
Keywords
abnormal interactions, feature exaction, Bayesian hierarchical network.
Abstract
Detecting the abnormal human interactions is vital in our daily life, especially when the society pay more attention to public security. But most researches didn't spare enough attention on abnormal interactions. In this paper, salient features are extracted for abnormal interactions, and the amounts of features are reduced to decrease the computation burden. Based on the extracted features, Bayesian hierarchical network is applied to estimating the pose of both persons. Then the corresponding rules for abnormal interaction detection are proposed. Finally, detection results are achieved based on the rules. UT-Interaction dataset is used for experiments. And the results show that the method outperforms with other methods in precision and sensitivity.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.66How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Ye Su
AU  - JianXin Song
PY  - 2016/09
DA  - 2016/09
TI  - A detection method based on Bayesian hierarchical network for abnormal interaction
BT  - 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.66
DO  - https://doi.org/10.2991/amitp-16.2016.66
ID  - Su2016/09
ER  -