Learning deep latent space for unsupervised violence detection

Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine visio...
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Autor*in:	Ehsan, Tahereh Zarrat [verfasserIn] Nahvi, Manoochehr Mohtavipour, Seyed Mehdi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Action recognition Human behavior analysis Human action recognition Violence detection Security and surveillance

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 82(2022), 8 vom: 19. Sept., Seite 12493-12512
Übergeordnetes Werk:	volume:82 ; year:2022 ; number:8 ; day:19 ; month:09 ; pages:12493-12512

Links:	Volltext

DOI / URN:	10.1007/s11042-022-13827-7

Katalog-ID:	SPR049537652

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520			\|a Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality.
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allfields	10.1007/s11042-022-13827-7 doi (DE-627)SPR049537652 (SPR)s11042-022-13827-7-e DE-627 ger DE-627 rakwb eng Ehsan, Tahereh Zarrat verfasserin aut Learning deep latent space for unsupervised violence detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. Action recognition (dpeaa)DE-He213 Human behavior analysis (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Violence detection (dpeaa)DE-He213 Security and surveillance (dpeaa)DE-He213 Nahvi, Manoochehr (orcid)0000-0001-9846-314X aut Mohtavipour, Seyed Mehdi aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 8 vom: 19. Sept., Seite 12493-12512 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512 https://dx.doi.org/10.1007/s11042-022-13827-7 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 8 19 09 12493-12512
spelling	10.1007/s11042-022-13827-7 doi (DE-627)SPR049537652 (SPR)s11042-022-13827-7-e DE-627 ger DE-627 rakwb eng Ehsan, Tahereh Zarrat verfasserin aut Learning deep latent space for unsupervised violence detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. Action recognition (dpeaa)DE-He213 Human behavior analysis (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Violence detection (dpeaa)DE-He213 Security and surveillance (dpeaa)DE-He213 Nahvi, Manoochehr (orcid)0000-0001-9846-314X aut Mohtavipour, Seyed Mehdi aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 8 vom: 19. Sept., Seite 12493-12512 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512 https://dx.doi.org/10.1007/s11042-022-13827-7 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 8 19 09 12493-12512
allfields_unstemmed	10.1007/s11042-022-13827-7 doi (DE-627)SPR049537652 (SPR)s11042-022-13827-7-e DE-627 ger DE-627 rakwb eng Ehsan, Tahereh Zarrat verfasserin aut Learning deep latent space for unsupervised violence detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. Action recognition (dpeaa)DE-He213 Human behavior analysis (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Violence detection (dpeaa)DE-He213 Security and surveillance (dpeaa)DE-He213 Nahvi, Manoochehr (orcid)0000-0001-9846-314X aut Mohtavipour, Seyed Mehdi aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 8 vom: 19. Sept., Seite 12493-12512 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512 https://dx.doi.org/10.1007/s11042-022-13827-7 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 8 19 09 12493-12512
allfieldsGer	10.1007/s11042-022-13827-7 doi (DE-627)SPR049537652 (SPR)s11042-022-13827-7-e DE-627 ger DE-627 rakwb eng Ehsan, Tahereh Zarrat verfasserin aut Learning deep latent space for unsupervised violence detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. Action recognition (dpeaa)DE-He213 Human behavior analysis (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Violence detection (dpeaa)DE-He213 Security and surveillance (dpeaa)DE-He213 Nahvi, Manoochehr (orcid)0000-0001-9846-314X aut Mohtavipour, Seyed Mehdi aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 8 vom: 19. Sept., Seite 12493-12512 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512 https://dx.doi.org/10.1007/s11042-022-13827-7 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 8 19 09 12493-12512
allfieldsSound	10.1007/s11042-022-13827-7 doi (DE-627)SPR049537652 (SPR)s11042-022-13827-7-e DE-627 ger DE-627 rakwb eng Ehsan, Tahereh Zarrat verfasserin aut Learning deep latent space for unsupervised violence detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. Action recognition (dpeaa)DE-He213 Human behavior analysis (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Violence detection (dpeaa)DE-He213 Security and surveillance (dpeaa)DE-He213 Nahvi, Manoochehr (orcid)0000-0001-9846-314X aut Mohtavipour, Seyed Mehdi aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2022), 8 vom: 19. Sept., Seite 12493-12512 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512 https://dx.doi.org/10.1007/s11042-022-13827-7 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 8 19 09 12493-12512
language	English
source	Enthalten in Multimedia tools and applications 82(2022), 8 vom: 19. Sept., Seite 12493-12512 volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512
sourceStr	Enthalten in Multimedia tools and applications 82(2022), 8 vom: 19. Sept., Seite 12493-12512 volume:82 year:2022 number:8 day:19 month:09 pages:12493-12512
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Action recognition Human behavior analysis Human action recognition Violence detection Security and surveillance
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Ehsan, Tahereh Zarrat @@aut@@ Nahvi, Manoochehr @@aut@@ Mohtavipour, Seyed Mehdi @@aut@@
publishDateDaySort_date	2022-09-19T00:00:00Z
hierarchy_top_id	27135030X
id	SPR049537652
language_de	englisch
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author	Ehsan, Tahereh Zarrat
spellingShingle	Ehsan, Tahereh Zarrat misc Action recognition misc Human behavior analysis misc Human action recognition misc Violence detection misc Security and surveillance Learning deep latent space for unsupervised violence detection
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topic_title	Learning deep latent space for unsupervised violence detection Action recognition (dpeaa)DE-He213 Human behavior analysis (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Violence detection (dpeaa)DE-He213 Security and surveillance (dpeaa)DE-He213
topic	misc Action recognition misc Human behavior analysis misc Human action recognition misc Violence detection misc Security and surveillance
topic_unstemmed	misc Action recognition misc Human behavior analysis misc Human action recognition misc Violence detection misc Security and surveillance
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title	Learning deep latent space for unsupervised violence detection
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title_full	Learning deep latent space for unsupervised violence detection
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author_browse	Ehsan, Tahereh Zarrat Nahvi, Manoochehr Mohtavipour, Seyed Mehdi
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title_sort	learning deep latent space for unsupervised violence detection
title_auth	Learning deep latent space for unsupervised violence detection
abstract	Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Learning deep latent space for unsupervised violence detection
url	https://dx.doi.org/10.1007/s11042-022-13827-7
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author2	Nahvi, Manoochehr Mohtavipour, Seyed Mehdi
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up_date	2024-07-04T01:13:48.783Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049537652</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510064610.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230304s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-022-13827-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049537652</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-022-13827-7-e</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="100" ind1="1" ind2=" "><subfield code="a">Ehsan, Tahereh Zarrat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Learning deep latent space for unsupervised violence detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Numerous violent actions occur in the world every day, affecting victims mentally and physically. To reduce violence rates in society, an automatic system may be required to analyze human activities quickly and detect violent actions accurately. Violence detection is a complex machine vision problem involving insufficient violent datasets and wide variation in activities and environments. In this paper, an unsupervised framework is presented to discriminate between normal and violent actions overcoming these challenges. This is accomplished by analyzing the latent space of a double-stream convolutional AutoEncoder (AE). In the proposed framework, the input samples are processed to extract discriminative spatial and temporal information. A human detection approach is applied in the spatial stream to remove background environment and other noisy information from video segments. Since motion patterns in violent actions are entirely different from normal actions, movement information is processed with a novel Jerk feature in the temporal stream. This feature describes the long-term motion acceleration and is composed of 7 consecutive frames. Moreover, the classification stage is carried out with a one-class classifier using the latent space of AEs to identify the outliers as violent samples. Extensive experiments on Hockey and Movies datasets showed that the proposed framework surpassed the previous works in terms of accuracy and generality.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Action recognition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Human behavior analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Human action recognition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Violence detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Security and surveillance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nahvi, Manoochehr</subfield><subfield code="0">(orcid)0000-0001-9846-314X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mohtavipour, Seyed Mehdi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">82(2022), 8 vom: 19. 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