Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder

Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is cr...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhou, Fuqiang [verfasserIn] Wang, Lin [verfasserIn] Li, Zuoxin [verfasserIn] Zuo, Wangxia [verfasserIn] Tan, Haishu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Autoencoder LSTM Abnormality detection

Übergeordnetes Werk:	Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 52(2019), 2 vom: 04. Okt., Seite 961-975
Übergeordnetes Werk:	volume:52 ; year:2019 ; number:2 ; day:04 ; month:10 ; pages:961-975

Links:	Volltext

DOI / URN:	10.1007/s11063-019-10113-w

Katalog-ID:	SPR041146875

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520			\|a Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.
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allfields	10.1007/s11063-019-10113-w doi (DE-627)SPR041146875 (SPR)s11063-019-10113-w-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Zhou, Fuqiang verfasserin aut Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets. Autoencoder (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Abnormality detection (dpeaa)DE-He213 Wang, Lin verfasserin aut Li, Zuoxin verfasserin aut Zuo, Wangxia verfasserin aut Tan, Haishu verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2019), 2 vom: 04. Okt., Seite 961-975 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2019 number:2 day:04 month:10 pages:961-975 https://dx.doi.org/10.1007/s11063-019-10113-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2019 2 04 10 961-975
spelling	10.1007/s11063-019-10113-w doi (DE-627)SPR041146875 (SPR)s11063-019-10113-w-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Zhou, Fuqiang verfasserin aut Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets. Autoencoder (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Abnormality detection (dpeaa)DE-He213 Wang, Lin verfasserin aut Li, Zuoxin verfasserin aut Zuo, Wangxia verfasserin aut Tan, Haishu verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2019), 2 vom: 04. Okt., Seite 961-975 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2019 number:2 day:04 month:10 pages:961-975 https://dx.doi.org/10.1007/s11063-019-10113-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2019 2 04 10 961-975
allfields_unstemmed	10.1007/s11063-019-10113-w doi (DE-627)SPR041146875 (SPR)s11063-019-10113-w-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Zhou, Fuqiang verfasserin aut Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets. Autoencoder (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Abnormality detection (dpeaa)DE-He213 Wang, Lin verfasserin aut Li, Zuoxin verfasserin aut Zuo, Wangxia verfasserin aut Tan, Haishu verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2019), 2 vom: 04. Okt., Seite 961-975 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2019 number:2 day:04 month:10 pages:961-975 https://dx.doi.org/10.1007/s11063-019-10113-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2019 2 04 10 961-975
allfieldsGer	10.1007/s11063-019-10113-w doi (DE-627)SPR041146875 (SPR)s11063-019-10113-w-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Zhou, Fuqiang verfasserin aut Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets. Autoencoder (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Abnormality detection (dpeaa)DE-He213 Wang, Lin verfasserin aut Li, Zuoxin verfasserin aut Zuo, Wangxia verfasserin aut Tan, Haishu verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2019), 2 vom: 04. Okt., Seite 961-975 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2019 number:2 day:04 month:10 pages:961-975 https://dx.doi.org/10.1007/s11063-019-10113-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2019 2 04 10 961-975
allfieldsSound	10.1007/s11063-019-10113-w doi (DE-627)SPR041146875 (SPR)s11063-019-10113-w-e DE-627 ger DE-627 rakwb eng 000 ASE 54.72 bkl Zhou, Fuqiang verfasserin aut Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets. Autoencoder (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Abnormality detection (dpeaa)DE-He213 Wang, Lin verfasserin aut Li, Zuoxin verfasserin aut Zuo, Wangxia verfasserin aut Tan, Haishu verfasserin aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 52(2019), 2 vom: 04. Okt., Seite 961-975 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:52 year:2019 number:2 day:04 month:10 pages:961-975 https://dx.doi.org/10.1007/s11063-019-10113-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 52 2019 2 04 10 961-975
language	English
source	Enthalten in Neural processing letters 52(2019), 2 vom: 04. Okt., Seite 961-975 volume:52 year:2019 number:2 day:04 month:10 pages:961-975
sourceStr	Enthalten in Neural processing letters 52(2019), 2 vom: 04. Okt., Seite 961-975 volume:52 year:2019 number:2 day:04 month:10 pages:961-975
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Autoencoder LSTM Abnormality detection
dewey-raw	000
isfreeaccess_bool	false
container_title	Neural processing letters
authorswithroles_txt_mv	Zhou, Fuqiang @@aut@@ Wang, Lin @@aut@@ Li, Zuoxin @@aut@@ Zuo, Wangxia @@aut@@ Tan, Haishu @@aut@@
publishDateDaySort_date	2019-10-04T00:00:00Z
hierarchy_top_id	270932607
dewey-sort	0
id	SPR041146875
language_de	englisch
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author	Zhou, Fuqiang
spellingShingle	Zhou, Fuqiang ddc 000 bkl 54.72 misc Autoencoder misc LSTM misc Abnormality detection Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder
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topic_title	000 ASE 54.72 bkl Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder Autoencoder (dpeaa)DE-He213 LSTM (dpeaa)DE-He213 Abnormality detection (dpeaa)DE-He213
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title	Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder
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title_full	Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder
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title_sort	unsupervised learning approach for abnormal event detection in surveillance video by hybrid autoencoder
title_auth	Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder
abstract	Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.
abstractGer	Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.
abstract_unstemmed	Abstract Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.
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title_short	Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder
url	https://dx.doi.org/10.1007/s11063-019-10113-w
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author2	Wang, Lin Li, Zuoxin Zuo, Wangxia Tan, Haishu
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up_date	2024-07-03T20:30:49.097Z
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Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder

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