Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition

Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationshi...
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Autor*in:	Ziliang Ren [verfasserIn] Huaqiang Yuan [verfasserIn] Wenhong Wei [verfasserIn] Tiezhu Zhao [verfasserIn] Qieshi Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets

Übergeordnetes Werk:	In: Electronics Letters - Wiley, 2021, 58(2022), 20, Seite 765-767
Übergeordnetes Werk:	volume:58 ; year:2022 ; number:20 ; pages:765-767

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1049/ell2.12597

Katalog-ID:	DOAJ022719237

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520			\|a Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy.
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allfields	10.1049/ell2.12597 doi (DE-627)DOAJ022719237 (DE-599)DOAJcaed5d9a90be4e95959cda144a4457c7 DE-627 ger DE-627 rakwb eng TK1-9971 Ziliang Ren verfasserin aut Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy. Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets Electrical engineering. Electronics. Nuclear engineering Huaqiang Yuan verfasserin aut Wenhong Wei verfasserin aut Tiezhu Zhao verfasserin aut Qieshi Zhang verfasserin aut In Electronics Letters Wiley, 2021 58(2022), 20, Seite 765-767 (DE-627)325616094 (DE-600)2038620-5 1350911X nnns volume:58 year:2022 number:20 pages:765-767 https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/article/caed5d9a90be4e95959cda144a4457c7 kostenfrei https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/toc/0013-5194 Journal toc kostenfrei https://doaj.org/toc/1350-911X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2022 20 765-767
spelling	10.1049/ell2.12597 doi (DE-627)DOAJ022719237 (DE-599)DOAJcaed5d9a90be4e95959cda144a4457c7 DE-627 ger DE-627 rakwb eng TK1-9971 Ziliang Ren verfasserin aut Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy. Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets Electrical engineering. Electronics. Nuclear engineering Huaqiang Yuan verfasserin aut Wenhong Wei verfasserin aut Tiezhu Zhao verfasserin aut Qieshi Zhang verfasserin aut In Electronics Letters Wiley, 2021 58(2022), 20, Seite 765-767 (DE-627)325616094 (DE-600)2038620-5 1350911X nnns volume:58 year:2022 number:20 pages:765-767 https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/article/caed5d9a90be4e95959cda144a4457c7 kostenfrei https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/toc/0013-5194 Journal toc kostenfrei https://doaj.org/toc/1350-911X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2022 20 765-767
allfields_unstemmed	10.1049/ell2.12597 doi (DE-627)DOAJ022719237 (DE-599)DOAJcaed5d9a90be4e95959cda144a4457c7 DE-627 ger DE-627 rakwb eng TK1-9971 Ziliang Ren verfasserin aut Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy. Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets Electrical engineering. Electronics. Nuclear engineering Huaqiang Yuan verfasserin aut Wenhong Wei verfasserin aut Tiezhu Zhao verfasserin aut Qieshi Zhang verfasserin aut In Electronics Letters Wiley, 2021 58(2022), 20, Seite 765-767 (DE-627)325616094 (DE-600)2038620-5 1350911X nnns volume:58 year:2022 number:20 pages:765-767 https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/article/caed5d9a90be4e95959cda144a4457c7 kostenfrei https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/toc/0013-5194 Journal toc kostenfrei https://doaj.org/toc/1350-911X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2022 20 765-767
allfieldsGer	10.1049/ell2.12597 doi (DE-627)DOAJ022719237 (DE-599)DOAJcaed5d9a90be4e95959cda144a4457c7 DE-627 ger DE-627 rakwb eng TK1-9971 Ziliang Ren verfasserin aut Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy. Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets Electrical engineering. Electronics. Nuclear engineering Huaqiang Yuan verfasserin aut Wenhong Wei verfasserin aut Tiezhu Zhao verfasserin aut Qieshi Zhang verfasserin aut In Electronics Letters Wiley, 2021 58(2022), 20, Seite 765-767 (DE-627)325616094 (DE-600)2038620-5 1350911X nnns volume:58 year:2022 number:20 pages:765-767 https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/article/caed5d9a90be4e95959cda144a4457c7 kostenfrei https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/toc/0013-5194 Journal toc kostenfrei https://doaj.org/toc/1350-911X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2022 20 765-767
allfieldsSound	10.1049/ell2.12597 doi (DE-627)DOAJ022719237 (DE-599)DOAJcaed5d9a90be4e95959cda144a4457c7 DE-627 ger DE-627 rakwb eng TK1-9971 Ziliang Ren verfasserin aut Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy. Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets Electrical engineering. Electronics. Nuclear engineering Huaqiang Yuan verfasserin aut Wenhong Wei verfasserin aut Tiezhu Zhao verfasserin aut Qieshi Zhang verfasserin aut In Electronics Letters Wiley, 2021 58(2022), 20, Seite 765-767 (DE-627)325616094 (DE-600)2038620-5 1350911X nnns volume:58 year:2022 number:20 pages:765-767 https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/article/caed5d9a90be4e95959cda144a4457c7 kostenfrei https://doi.org/10.1049/ell2.12597 kostenfrei https://doaj.org/toc/0013-5194 Journal toc kostenfrei https://doaj.org/toc/1350-911X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 58 2022 20 765-767
language	English
source	In Electronics Letters 58(2022), 20, Seite 765-767 volume:58 year:2022 number:20 pages:765-767
sourceStr	In Electronics Letters 58(2022), 20, Seite 765-767 volume:58 year:2022 number:20 pages:765-767
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	Electronics Letters
authorswithroles_txt_mv	Ziliang Ren @@aut@@ Huaqiang Yuan @@aut@@ Wenhong Wei @@aut@@ Tiezhu Zhao @@aut@@ Qieshi Zhang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	325616094
id	DOAJ022719237
language_de	englisch
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callnumber-first	T - Technology
author	Ziliang Ren
spellingShingle	Ziliang Ren misc TK1-9971 misc Image recognition misc Optimisation techniques misc Computer vision and image processing techniques misc Video signal processing misc Neural nets misc Electrical engineering. Electronics. Nuclear engineering Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition
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topic_title	TK1-9971 Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition Image recognition Optimisation techniques Computer vision and image processing techniques Video signal processing Neural nets
topic	misc TK1-9971 misc Image recognition misc Optimisation techniques misc Computer vision and image processing techniques misc Video signal processing misc Neural nets misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Image recognition misc Optimisation techniques misc Computer vision and image processing techniques misc Video signal processing misc Neural nets misc Electrical engineering. Electronics. Nuclear engineering
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title	Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition
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title_full	Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition
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author_browse	Ziliang Ren Huaqiang Yuan Wenhong Wei Tiezhu Zhao Qieshi Zhang
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title_sort	convolutional non‐local spatial‐temporal learning for multi‐modality action recognition
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title_auth	Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition
abstract	Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy.
abstractGer	Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy.
abstract_unstemmed	Abstract Traditional deep convolutional networks have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single convolutional networks to extract the underlying relationship and complementary features between these two kinds of modalities. The authors proposed a novel two stream convolutional networks for multi‐modality action recognition by joint optimisation learning to extract global features from RGB and depth sequences. Specifically, a non‐local multi‐modality compensation block is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi‐modality human action datasets, including NTU RGB+D 120 and PKU‐MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed non‐local multi‐modality compensation block can learn complementary features and enhance the recognition accuracy.
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title_short	Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition
url	https://doi.org/10.1049/ell2.12597 https://doaj.org/article/caed5d9a90be4e95959cda144a4457c7 https://doaj.org/toc/0013-5194 https://doaj.org/toc/1350-911X
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Convolutional non‐local spatial‐temporal learning for multi‐modality action recognition

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