Fast Abnormal Event Detection
Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lu, Cewu [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Springer US, 1987, 127(2018), 8 vom: 01. Dez., Seite 993-1011 |
|---|---|
| Übergeordnetes Werk: |
volume:127 ; year:2018 ; number:8 ; day:01 ; month:12 ; pages:993-1011 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11263-018-1129-8 |
|---|
| Katalog-ID: |
OLC2057752917 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2057752917 | ||
| 003 | DE-627 | ||
| 005 | 20230504072233.0 | ||
| 007 | tu | ||
| 008 | 200819s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11263-018-1129-8 |2 doi | |
| 035 | |a (DE-627)OLC2057752917 | ||
| 035 | |a (DE-He213)s11263-018-1129-8-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Lu, Cewu |e verfasserin |0 (orcid)0000-0003-1533-8576 |4 aut | |
| 245 | 1 | 0 | |a Fast Abnormal Event Detection |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2018 | ||
| 520 | |a Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. | ||
| 650 | 4 | |a Abnormal event | |
| 650 | 4 | |a Realtime detection | |
| 650 | 4 | |a Event detection | |
| 650 | 4 | |a Video analysis | |
| 700 | 1 | |a Shi, Jianping |4 aut | |
| 700 | 1 | |a Wang, Weiming |4 aut | |
| 700 | 1 | |a Jia, Jiaya |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of computer vision |d Springer US, 1987 |g 127(2018), 8 vom: 01. Dez., Seite 993-1011 |w (DE-627)129354252 |w (DE-600)155895-X |w (DE-576)018081428 |x 0920-5691 |7 nnns |
| 773 | 1 | 8 | |g volume:127 |g year:2018 |g number:8 |g day:01 |g month:12 |g pages:993-1011 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11263-018-1129-8 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2244 | ||
| 951 | |a AR | ||
| 952 | |d 127 |j 2018 |e 8 |b 01 |c 12 |h 993-1011 | ||
| author_variant |
c l cl j s js w w ww j j jj |
|---|---|
| matchkey_str |
article:09205691:2018----::atbomlvnd |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s11263-018-1129-8 doi (DE-627)OLC2057752917 (DE-He213)s11263-018-1129-8-p DE-627 ger DE-627 rakwb eng 004 VZ Lu, Cewu verfasserin (orcid)0000-0003-1533-8576 aut Fast Abnormal Event Detection 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. Abnormal event Realtime detection Event detection Video analysis Shi, Jianping aut Wang, Weiming aut Jia, Jiaya aut Enthalten in International journal of computer vision Springer US, 1987 127(2018), 8 vom: 01. Dez., Seite 993-1011 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 https://doi.org/10.1007/s11263-018-1129-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2244 AR 127 2018 8 01 12 993-1011 |
| spelling |
10.1007/s11263-018-1129-8 doi (DE-627)OLC2057752917 (DE-He213)s11263-018-1129-8-p DE-627 ger DE-627 rakwb eng 004 VZ Lu, Cewu verfasserin (orcid)0000-0003-1533-8576 aut Fast Abnormal Event Detection 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. Abnormal event Realtime detection Event detection Video analysis Shi, Jianping aut Wang, Weiming aut Jia, Jiaya aut Enthalten in International journal of computer vision Springer US, 1987 127(2018), 8 vom: 01. Dez., Seite 993-1011 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 https://doi.org/10.1007/s11263-018-1129-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2244 AR 127 2018 8 01 12 993-1011 |
| allfields_unstemmed |
10.1007/s11263-018-1129-8 doi (DE-627)OLC2057752917 (DE-He213)s11263-018-1129-8-p DE-627 ger DE-627 rakwb eng 004 VZ Lu, Cewu verfasserin (orcid)0000-0003-1533-8576 aut Fast Abnormal Event Detection 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. Abnormal event Realtime detection Event detection Video analysis Shi, Jianping aut Wang, Weiming aut Jia, Jiaya aut Enthalten in International journal of computer vision Springer US, 1987 127(2018), 8 vom: 01. Dez., Seite 993-1011 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 https://doi.org/10.1007/s11263-018-1129-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2244 AR 127 2018 8 01 12 993-1011 |
| allfieldsGer |
10.1007/s11263-018-1129-8 doi (DE-627)OLC2057752917 (DE-He213)s11263-018-1129-8-p DE-627 ger DE-627 rakwb eng 004 VZ Lu, Cewu verfasserin (orcid)0000-0003-1533-8576 aut Fast Abnormal Event Detection 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. Abnormal event Realtime detection Event detection Video analysis Shi, Jianping aut Wang, Weiming aut Jia, Jiaya aut Enthalten in International journal of computer vision Springer US, 1987 127(2018), 8 vom: 01. Dez., Seite 993-1011 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 https://doi.org/10.1007/s11263-018-1129-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2244 AR 127 2018 8 01 12 993-1011 |
| allfieldsSound |
10.1007/s11263-018-1129-8 doi (DE-627)OLC2057752917 (DE-He213)s11263-018-1129-8-p DE-627 ger DE-627 rakwb eng 004 VZ Lu, Cewu verfasserin (orcid)0000-0003-1533-8576 aut Fast Abnormal Event Detection 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. Abnormal event Realtime detection Event detection Video analysis Shi, Jianping aut Wang, Weiming aut Jia, Jiaya aut Enthalten in International journal of computer vision Springer US, 1987 127(2018), 8 vom: 01. Dez., Seite 993-1011 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 https://doi.org/10.1007/s11263-018-1129-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2244 AR 127 2018 8 01 12 993-1011 |
| language |
English |
| source |
Enthalten in International journal of computer vision 127(2018), 8 vom: 01. Dez., Seite 993-1011 volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 |
| sourceStr |
Enthalten in International journal of computer vision 127(2018), 8 vom: 01. Dez., Seite 993-1011 volume:127 year:2018 number:8 day:01 month:12 pages:993-1011 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Abnormal event Realtime detection Event detection Video analysis |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
International journal of computer vision |
| authorswithroles_txt_mv |
Lu, Cewu @@aut@@ Shi, Jianping @@aut@@ Wang, Weiming @@aut@@ Jia, Jiaya @@aut@@ |
| publishDateDaySort_date |
2018-12-01T00:00:00Z |
| hierarchy_top_id |
129354252 |
| dewey-sort |
14 |
| id |
OLC2057752917 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2057752917</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504072233.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11263-018-1129-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2057752917</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11263-018-1129-8-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lu, Cewu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1533-8576</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fast Abnormal Event Detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Abnormal event</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Realtime detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Event detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Video analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shi, Jianping</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Weiming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jia, Jiaya</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computer vision</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">127(2018), 8 vom: 01. Dez., Seite 993-1011</subfield><subfield code="w">(DE-627)129354252</subfield><subfield code="w">(DE-600)155895-X</subfield><subfield code="w">(DE-576)018081428</subfield><subfield code="x">0920-5691</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:127</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:8</subfield><subfield code="g">day:01</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:993-1011</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11263-018-1129-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">127</subfield><subfield code="j">2018</subfield><subfield code="e">8</subfield><subfield code="b">01</subfield><subfield code="c">12</subfield><subfield code="h">993-1011</subfield></datafield></record></collection>
|
| author |
Lu, Cewu |
| spellingShingle |
Lu, Cewu ddc 004 misc Abnormal event misc Realtime detection misc Event detection misc Video analysis Fast Abnormal Event Detection |
| authorStr |
Lu, Cewu |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129354252 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0920-5691 |
| topic_title |
004 VZ Fast Abnormal Event Detection Abnormal event Realtime detection Event detection Video analysis |
| topic |
ddc 004 misc Abnormal event misc Realtime detection misc Event detection misc Video analysis |
| topic_unstemmed |
ddc 004 misc Abnormal event misc Realtime detection misc Event detection misc Video analysis |
| topic_browse |
ddc 004 misc Abnormal event misc Realtime detection misc Event detection misc Video analysis |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
International journal of computer vision |
| hierarchy_parent_id |
129354252 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
International journal of computer vision |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129354252 (DE-600)155895-X (DE-576)018081428 |
| title |
Fast Abnormal Event Detection |
| ctrlnum |
(DE-627)OLC2057752917 (DE-He213)s11263-018-1129-8-p |
| title_full |
Fast Abnormal Event Detection |
| author_sort |
Lu, Cewu |
| journal |
International journal of computer vision |
| journalStr |
International journal of computer vision |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
993 |
| author_browse |
Lu, Cewu Shi, Jianping Wang, Weiming Jia, Jiaya |
| container_volume |
127 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Lu, Cewu |
| doi_str_mv |
10.1007/s11263-018-1129-8 |
| normlink |
(ORCID)0000-0003-1533-8576 |
| normlink_prefix_str_mv |
(orcid)0000-0003-1533-8576 |
| dewey-full |
004 |
| title_sort |
fast abnormal event detection |
| title_auth |
Fast Abnormal Event Detection |
| abstract |
Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2244 |
| container_issue |
8 |
| title_short |
Fast Abnormal Event Detection |
| url |
https://doi.org/10.1007/s11263-018-1129-8 |
| remote_bool |
false |
| author2 |
Shi, Jianping Wang, Weiming Jia, Jiaya |
| author2Str |
Shi, Jianping Wang, Weiming Jia, Jiaya |
| ppnlink |
129354252 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11263-018-1129-8 |
| up_date |
2024-07-03T16:10:22.383Z |
| _version_ |
1803574860720373760 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2057752917</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504072233.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11263-018-1129-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2057752917</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11263-018-1129-8-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lu, Cewu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1533-8576</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fast Abnormal Event Detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Abnormal event</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Realtime detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Event detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Video analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shi, Jianping</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Weiming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jia, Jiaya</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computer vision</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">127(2018), 8 vom: 01. Dez., Seite 993-1011</subfield><subfield code="w">(DE-627)129354252</subfield><subfield code="w">(DE-600)155895-X</subfield><subfield code="w">(DE-576)018081428</subfield><subfield code="x">0920-5691</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:127</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:8</subfield><subfield code="g">day:01</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:993-1011</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11263-018-1129-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">127</subfield><subfield code="j">2018</subfield><subfield code="e">8</subfield><subfield code="b">01</subfield><subfield code="c">12</subfield><subfield code="h">993-1011</subfield></datafield></record></collection>
|
| score |
7.399687 |