Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder

With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of...
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Autor*in:	Lin Wang [verfasserIn] Haishu Tan [verfasserIn] Fuqiang Zhou [verfasserIn] Wangxia Zuo [verfasserIn] Pengfei Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 44278-44289
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:44278-44289

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3165977

Katalog-ID:	DOAJ020942389

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allfields	10.1109/ACCESS.2022.3165977 doi (DE-627)DOAJ020942389 (DE-599)DOAJf1e45b88a6014513b657570de03960e4 DE-627 ger DE-627 rakwb eng TK1-9971 Lin Wang verfasserin aut Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count. Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection Electrical engineering. Electronics. Nuclear engineering Haishu Tan verfasserin aut Fuqiang Zhou verfasserin aut Wangxia Zuo verfasserin aut Pengfei Sun verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 44278-44289 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:44278-44289 https://doi.org/10.1109/ACCESS.2022.3165977 kostenfrei https://doaj.org/article/f1e45b88a6014513b657570de03960e4 kostenfrei https://ieeexplore.ieee.org/document/9758677/ 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 10 2022 44278-44289
spelling	10.1109/ACCESS.2022.3165977 doi (DE-627)DOAJ020942389 (DE-599)DOAJf1e45b88a6014513b657570de03960e4 DE-627 ger DE-627 rakwb eng TK1-9971 Lin Wang verfasserin aut Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count. Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection Electrical engineering. Electronics. Nuclear engineering Haishu Tan verfasserin aut Fuqiang Zhou verfasserin aut Wangxia Zuo verfasserin aut Pengfei Sun verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 44278-44289 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:44278-44289 https://doi.org/10.1109/ACCESS.2022.3165977 kostenfrei https://doaj.org/article/f1e45b88a6014513b657570de03960e4 kostenfrei https://ieeexplore.ieee.org/document/9758677/ 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 10 2022 44278-44289
allfields_unstemmed	10.1109/ACCESS.2022.3165977 doi (DE-627)DOAJ020942389 (DE-599)DOAJf1e45b88a6014513b657570de03960e4 DE-627 ger DE-627 rakwb eng TK1-9971 Lin Wang verfasserin aut Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count. Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection Electrical engineering. Electronics. Nuclear engineering Haishu Tan verfasserin aut Fuqiang Zhou verfasserin aut Wangxia Zuo verfasserin aut Pengfei Sun verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 44278-44289 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:44278-44289 https://doi.org/10.1109/ACCESS.2022.3165977 kostenfrei https://doaj.org/article/f1e45b88a6014513b657570de03960e4 kostenfrei https://ieeexplore.ieee.org/document/9758677/ 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 10 2022 44278-44289
allfieldsGer	10.1109/ACCESS.2022.3165977 doi (DE-627)DOAJ020942389 (DE-599)DOAJf1e45b88a6014513b657570de03960e4 DE-627 ger DE-627 rakwb eng TK1-9971 Lin Wang verfasserin aut Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count. Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection Electrical engineering. Electronics. Nuclear engineering Haishu Tan verfasserin aut Fuqiang Zhou verfasserin aut Wangxia Zuo verfasserin aut Pengfei Sun verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 44278-44289 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:44278-44289 https://doi.org/10.1109/ACCESS.2022.3165977 kostenfrei https://doaj.org/article/f1e45b88a6014513b657570de03960e4 kostenfrei https://ieeexplore.ieee.org/document/9758677/ 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 10 2022 44278-44289
allfieldsSound	10.1109/ACCESS.2022.3165977 doi (DE-627)DOAJ020942389 (DE-599)DOAJf1e45b88a6014513b657570de03960e4 DE-627 ger DE-627 rakwb eng TK1-9971 Lin Wang verfasserin aut Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count. Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection Electrical engineering. Electronics. Nuclear engineering Haishu Tan verfasserin aut Fuqiang Zhou verfasserin aut Wangxia Zuo verfasserin aut Pengfei Sun verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 44278-44289 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:44278-44289 https://doi.org/10.1109/ACCESS.2022.3165977 kostenfrei https://doaj.org/article/f1e45b88a6014513b657570de03960e4 kostenfrei https://ieeexplore.ieee.org/document/9758677/ 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 10 2022 44278-44289
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source	In IEEE Access 10(2022), Seite 44278-44289 volume:10 year:2022 pages:44278-44289
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callnumber-first	T - Technology
author	Lin Wang
spellingShingle	Lin Wang misc TK1-9971 misc Autoencoder misc variational autoencoder misc LSTM misc ConvLSTM misc anomaly detection misc Electrical engineering. Electronics. Nuclear engineering Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
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topic_title	TK1-9971 Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder Autoencoder variational autoencoder LSTM ConvLSTM anomaly detection
topic	misc TK1-9971 misc Autoencoder misc variational autoencoder misc LSTM misc ConvLSTM misc anomaly detection misc Electrical engineering. Electronics. Nuclear engineering
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title	Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
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title_auth	Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
abstract	With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count.
abstractGer	With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count.
abstract_unstemmed	With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. Variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count.
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title_short	Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
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Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder

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