A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction

Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction me...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Neda Taghinezhad [verfasserIn] Mehran Yazdi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Video anomaly detection U-Net future frame prediction memory networks surveillance videos

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 9295-9310
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:9295-9310

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3237028

Katalog-ID:	DOAJ081265697

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ081265697
003	DE-627
005	20230310211431.0
007	cr uuu---uuuuu
008	230310s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2023.3237028 \|2 doi
035			\|a (DE-627)DOAJ081265697
035			\|a (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Neda Taghinezhad \|e verfasserin \|4 aut
245	1	2	\|a A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.
650		4	\|a Video anomaly detection
650		4	\|a U-Net
650		4	\|a future frame prediction
650		4	\|a memory networks
650		4	\|a surveillance videos
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Mehran Yazdi \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 11(2023), Seite 9295-9310 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:11 \|g year:2023 \|g pages:9295-9310
856	4	0	\|u https://doi.org/10.1109/ACCESS.2023.3237028 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/10017243/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 11 \|j 2023 \|h 9295-9310

Indexfelder

author_variant	n t nt m y my
matchkey_str	article:21693536:2023----::nwnuevsdieaoayeetouigutsaeetrmmrztoadutptt
hierarchy_sort_str	2023
callnumber-subject-code	TK
publishDate	2023
allfields	10.1109/ACCESS.2023.3237028 doi (DE-627)DOAJ081265697 (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62 DE-627 ger DE-627 rakwb eng TK1-9971 Neda Taghinezhad verfasserin aut A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models. Video anomaly detection U-Net future frame prediction memory networks surveillance videos Electrical engineering. Electronics. Nuclear engineering Mehran Yazdi verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 9295-9310 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:9295-9310 https://doi.org/10.1109/ACCESS.2023.3237028 kostenfrei https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 kostenfrei https://ieeexplore.ieee.org/document/10017243/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9295-9310
spelling	10.1109/ACCESS.2023.3237028 doi (DE-627)DOAJ081265697 (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62 DE-627 ger DE-627 rakwb eng TK1-9971 Neda Taghinezhad verfasserin aut A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models. Video anomaly detection U-Net future frame prediction memory networks surveillance videos Electrical engineering. Electronics. Nuclear engineering Mehran Yazdi verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 9295-9310 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:9295-9310 https://doi.org/10.1109/ACCESS.2023.3237028 kostenfrei https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 kostenfrei https://ieeexplore.ieee.org/document/10017243/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9295-9310
allfields_unstemmed	10.1109/ACCESS.2023.3237028 doi (DE-627)DOAJ081265697 (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62 DE-627 ger DE-627 rakwb eng TK1-9971 Neda Taghinezhad verfasserin aut A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models. Video anomaly detection U-Net future frame prediction memory networks surveillance videos Electrical engineering. Electronics. Nuclear engineering Mehran Yazdi verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 9295-9310 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:9295-9310 https://doi.org/10.1109/ACCESS.2023.3237028 kostenfrei https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 kostenfrei https://ieeexplore.ieee.org/document/10017243/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9295-9310
allfieldsGer	10.1109/ACCESS.2023.3237028 doi (DE-627)DOAJ081265697 (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62 DE-627 ger DE-627 rakwb eng TK1-9971 Neda Taghinezhad verfasserin aut A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models. Video anomaly detection U-Net future frame prediction memory networks surveillance videos Electrical engineering. Electronics. Nuclear engineering Mehran Yazdi verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 9295-9310 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:9295-9310 https://doi.org/10.1109/ACCESS.2023.3237028 kostenfrei https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 kostenfrei https://ieeexplore.ieee.org/document/10017243/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9295-9310
allfieldsSound	10.1109/ACCESS.2023.3237028 doi (DE-627)DOAJ081265697 (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62 DE-627 ger DE-627 rakwb eng TK1-9971 Neda Taghinezhad verfasserin aut A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models. Video anomaly detection U-Net future frame prediction memory networks surveillance videos Electrical engineering. Electronics. Nuclear engineering Mehran Yazdi verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 9295-9310 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:9295-9310 https://doi.org/10.1109/ACCESS.2023.3237028 kostenfrei https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 kostenfrei https://ieeexplore.ieee.org/document/10017243/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9295-9310
language	English
source	In IEEE Access 11(2023), Seite 9295-9310 volume:11 year:2023 pages:9295-9310
sourceStr	In IEEE Access 11(2023), Seite 9295-9310 volume:11 year:2023 pages:9295-9310
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Video anomaly detection U-Net future frame prediction memory networks surveillance videos Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Neda Taghinezhad @@aut@@ Mehran Yazdi @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ081265697
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ081265697</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310211431.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2023.3237028</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ081265697</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Neda Taghinezhad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Video anomaly detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">U-Net</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">future frame prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">memory networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">surveillance videos</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mehran Yazdi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">11(2023), Seite 9295-9310</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield><subfield code="g">pages:9295-9310</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2023.3237028</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/10017243/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</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_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="h">9295-9310</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Neda Taghinezhad
spellingShingle	Neda Taghinezhad misc TK1-9971 misc Video anomaly detection misc U-Net misc future frame prediction misc memory networks misc surveillance videos misc Electrical engineering. Electronics. Nuclear engineering A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction
authorStr	Neda Taghinezhad
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction Video anomaly detection U-Net future frame prediction memory networks surveillance videos
topic	misc TK1-9971 misc Video anomaly detection misc U-Net misc future frame prediction misc memory networks misc surveillance videos misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Video anomaly detection misc U-Net misc future frame prediction misc memory networks misc surveillance videos misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Video anomaly detection misc U-Net misc future frame prediction misc memory networks misc surveillance videos misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction
ctrlnum	(DE-627)DOAJ081265697 (DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62
title_full	A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction
author_sort	Neda Taghinezhad
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
container_start_page	9295
author_browse	Neda Taghinezhad Mehran Yazdi
container_volume	11
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Neda Taghinezhad
doi_str_mv	10.1109/ACCESS.2023.3237028
author2-role	verfasserin
title_sort	new unsupervised video anomaly detection using multi-scale feature memorization and multipath temporal information prediction
callnumber	TK1-9971
title_auth	A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction
abstract	Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.
abstractGer	Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.
abstract_unstemmed	Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction
url	https://doi.org/10.1109/ACCESS.2023.3237028 https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62 https://ieeexplore.ieee.org/document/10017243/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Mehran Yazdi
author2Str	Mehran Yazdi
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2023.3237028
callnumber-a	TK1-9971
up_date	2024-07-03T19:09:41.204Z
_version_	1803586142161862656
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ081265697</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310211431.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2023.3237028</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ081265697</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ42e3f3203e6a4ec5a19d4d2701543f62</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Neda Taghinezhad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Video anomaly detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">U-Net</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">future frame prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">memory networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">surveillance videos</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mehran Yazdi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">11(2023), Seite 9295-9310</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield><subfield code="g">pages:9295-9310</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2023.3237028</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/42e3f3203e6a4ec5a19d4d2701543f62</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/10017243/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</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_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="h">9295-9310</subfield></datafield></record></collection>
score	7.400299

Nicht das Richtige dabei?

Schreiben Sie uns!

A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?