Fast Anomaly Detection Based on 3D Integral Images
Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Shifeng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 54(2022), 2 vom: 19. Jan., Seite 1465-1479 |
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| Übergeordnetes Werk: |
volume:54 ; year:2022 ; number:2 ; day:19 ; month:01 ; pages:1465-1479 |
| Links: |
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| DOI / URN: |
10.1007/s11063-021-10691-8 |
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OLC2078441546 |
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| 520 | |a Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. | ||
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| 700 | 1 | |a Yang, Yuqiang |4 aut | |
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10.1007/s11063-021-10691-8 doi (DE-627)OLC2078441546 (DE-He213)s11063-021-10691-8-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Shifeng verfasserin (orcid)0000-0002-5220-5791 aut Fast Anomaly Detection Based on 3D Integral Images 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. Anomaly detection Integral image Bayesian framework Cheng, Yan aut Liu, Yunfeng aut Yang, Yuqiang aut Enthalten in Neural processing letters Springer US, 1994 54(2022), 2 vom: 19. Jan., Seite 1465-1479 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2022 number:2 day:19 month:01 pages:1465-1479 https://doi.org/10.1007/s11063-021-10691-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2022 2 19 01 1465-1479 |
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10.1007/s11063-021-10691-8 doi (DE-627)OLC2078441546 (DE-He213)s11063-021-10691-8-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Shifeng verfasserin (orcid)0000-0002-5220-5791 aut Fast Anomaly Detection Based on 3D Integral Images 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. Anomaly detection Integral image Bayesian framework Cheng, Yan aut Liu, Yunfeng aut Yang, Yuqiang aut Enthalten in Neural processing letters Springer US, 1994 54(2022), 2 vom: 19. Jan., Seite 1465-1479 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2022 number:2 day:19 month:01 pages:1465-1479 https://doi.org/10.1007/s11063-021-10691-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2022 2 19 01 1465-1479 |
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10.1007/s11063-021-10691-8 doi (DE-627)OLC2078441546 (DE-He213)s11063-021-10691-8-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Shifeng verfasserin (orcid)0000-0002-5220-5791 aut Fast Anomaly Detection Based on 3D Integral Images 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. Anomaly detection Integral image Bayesian framework Cheng, Yan aut Liu, Yunfeng aut Yang, Yuqiang aut Enthalten in Neural processing letters Springer US, 1994 54(2022), 2 vom: 19. Jan., Seite 1465-1479 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2022 number:2 day:19 month:01 pages:1465-1479 https://doi.org/10.1007/s11063-021-10691-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2022 2 19 01 1465-1479 |
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10.1007/s11063-021-10691-8 doi (DE-627)OLC2078441546 (DE-He213)s11063-021-10691-8-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Shifeng verfasserin (orcid)0000-0002-5220-5791 aut Fast Anomaly Detection Based on 3D Integral Images 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. Anomaly detection Integral image Bayesian framework Cheng, Yan aut Liu, Yunfeng aut Yang, Yuqiang aut Enthalten in Neural processing letters Springer US, 1994 54(2022), 2 vom: 19. Jan., Seite 1465-1479 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:54 year:2022 number:2 day:19 month:01 pages:1465-1479 https://doi.org/10.1007/s11063-021-10691-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 54 2022 2 19 01 1465-1479 |
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Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078441546</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506005113.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-021-10691-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078441546</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-021-10691-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Shifeng</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5220-5791</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fast Anomaly Detection Based on 3D Integral Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. 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