Computer vision-based approach for skeleton-based action recognition, SAHC
Abstract Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion cont...
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| Autor*in: |
Shujah Islam, M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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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| Übergeordnetes Werk: |
Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 18(2023), 2 vom: 11. Nov., Seite 1343-1354 |
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| Übergeordnetes Werk: |
volume:18 ; year:2023 ; number:2 ; day:11 ; month:11 ; pages:1343-1354 |
| Links: |
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| DOI / URN: |
10.1007/s11760-023-02829-z |
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| Katalog-ID: |
SPR054836409 |
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| 520 | |a Abstract Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. | ||
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| 650 | 4 | |a Skeleton-based action recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Human action recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Artificial intelligence |7 (dpeaa)DE-He213 | |
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10.1007/s11760-023-02829-z doi (DE-627)SPR054836409 (SPR)s11760-023-02829-z-e DE-627 ger DE-627 rakwb eng Shujah Islam, M. verfasserin aut Computer vision-based approach for skeleton-based action recognition, SAHC 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. Computer vision (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Skeleton-based action recognition (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 11. Nov., Seite 1343-1354 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:11 month:11 pages:1343-1354 https://dx.doi.org/10.1007/s11760-023-02829-z 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_65 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_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 18 2023 2 11 11 1343-1354 |
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10.1007/s11760-023-02829-z doi (DE-627)SPR054836409 (SPR)s11760-023-02829-z-e DE-627 ger DE-627 rakwb eng Shujah Islam, M. verfasserin aut Computer vision-based approach for skeleton-based action recognition, SAHC 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. Computer vision (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Skeleton-based action recognition (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 11. Nov., Seite 1343-1354 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:11 month:11 pages:1343-1354 https://dx.doi.org/10.1007/s11760-023-02829-z 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_65 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_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 18 2023 2 11 11 1343-1354 |
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10.1007/s11760-023-02829-z doi (DE-627)SPR054836409 (SPR)s11760-023-02829-z-e DE-627 ger DE-627 rakwb eng Shujah Islam, M. verfasserin aut Computer vision-based approach for skeleton-based action recognition, SAHC 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. Computer vision (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Skeleton-based action recognition (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 11. Nov., Seite 1343-1354 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:11 month:11 pages:1343-1354 https://dx.doi.org/10.1007/s11760-023-02829-z 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_65 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_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 18 2023 2 11 11 1343-1354 |
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10.1007/s11760-023-02829-z doi (DE-627)SPR054836409 (SPR)s11760-023-02829-z-e DE-627 ger DE-627 rakwb eng Shujah Islam, M. verfasserin aut Computer vision-based approach for skeleton-based action recognition, SAHC 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. Computer vision (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Skeleton-based action recognition (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 11. Nov., Seite 1343-1354 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:11 month:11 pages:1343-1354 https://dx.doi.org/10.1007/s11760-023-02829-z 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_65 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_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 18 2023 2 11 11 1343-1354 |
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10.1007/s11760-023-02829-z doi (DE-627)SPR054836409 (SPR)s11760-023-02829-z-e DE-627 ger DE-627 rakwb eng Shujah Islam, M. verfasserin aut Computer vision-based approach for skeleton-based action recognition, SAHC 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. Computer vision (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Skeleton-based action recognition (dpeaa)DE-He213 Human action recognition (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 11. Nov., Seite 1343-1354 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:11 month:11 pages:1343-1354 https://dx.doi.org/10.1007/s11760-023-02829-z 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_65 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_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 18 2023 2 11 11 1343-1354 |
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Computer vision-based approach for skeleton-based action recognition, SAHC |
| abstract |
Abstract Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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 Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial–temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial–temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) 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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Computer vision-based approach for skeleton-based action recognition, SAHC |
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https://dx.doi.org/10.1007/s11760-023-02829-z |
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10.1007/s11760-023-02829-z |
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| score |
7.399021 |