Action recognition based on adaptive region perception

Abstract Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal informatio...
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Autor*in:	Lu, Tongwei [verfasserIn] Yang, Qi Min, Feng Zhang, Yanduo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Temporal modelling Adaptive areas Long-range temporal information Short-range temporal information

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.

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 36(2023), 2 vom: 09. Okt., Seite 943-959
Übergeordnetes Werk:	volume:36 ; year:2023 ; number:2 ; day:09 ; month:10 ; pages:943-959

Links:	Volltext

DOI / URN:	10.1007/s00521-023-09069-9

Katalog-ID:	SPR054276055

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520			\|a Abstract Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance.
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allfields	10.1007/s00521-023-09069-9 doi (DE-627)SPR054276055 (SPR)s00521-023-09069-9-e DE-627 ger DE-627 rakwb eng Lu, Tongwei verfasserin (orcid)0000-0002-3900-6456 aut Action recognition based on adaptive region perception 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. Temporal modelling (dpeaa)DE-He213 Adaptive areas (dpeaa)DE-He213 Long-range temporal information (dpeaa)DE-He213 Short-range temporal information (dpeaa)DE-He213 Yang, Qi aut Min, Feng aut Zhang, Yanduo aut Enthalten in Neural computing & applications London : Springer, 1993 36(2023), 2 vom: 09. Okt., Seite 943-959 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2023 number:2 day:09 month:10 pages:943-959 https://dx.doi.org/10.1007/s00521-023-09069-9 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 36 2023 2 09 10 943-959
spelling	10.1007/s00521-023-09069-9 doi (DE-627)SPR054276055 (SPR)s00521-023-09069-9-e DE-627 ger DE-627 rakwb eng Lu, Tongwei verfasserin (orcid)0000-0002-3900-6456 aut Action recognition based on adaptive region perception 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. Temporal modelling (dpeaa)DE-He213 Adaptive areas (dpeaa)DE-He213 Long-range temporal information (dpeaa)DE-He213 Short-range temporal information (dpeaa)DE-He213 Yang, Qi aut Min, Feng aut Zhang, Yanduo aut Enthalten in Neural computing & applications London : Springer, 1993 36(2023), 2 vom: 09. Okt., Seite 943-959 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2023 number:2 day:09 month:10 pages:943-959 https://dx.doi.org/10.1007/s00521-023-09069-9 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 36 2023 2 09 10 943-959
allfields_unstemmed	10.1007/s00521-023-09069-9 doi (DE-627)SPR054276055 (SPR)s00521-023-09069-9-e DE-627 ger DE-627 rakwb eng Lu, Tongwei verfasserin (orcid)0000-0002-3900-6456 aut Action recognition based on adaptive region perception 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. Temporal modelling (dpeaa)DE-He213 Adaptive areas (dpeaa)DE-He213 Long-range temporal information (dpeaa)DE-He213 Short-range temporal information (dpeaa)DE-He213 Yang, Qi aut Min, Feng aut Zhang, Yanduo aut Enthalten in Neural computing & applications London : Springer, 1993 36(2023), 2 vom: 09. Okt., Seite 943-959 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2023 number:2 day:09 month:10 pages:943-959 https://dx.doi.org/10.1007/s00521-023-09069-9 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 36 2023 2 09 10 943-959
allfieldsGer	10.1007/s00521-023-09069-9 doi (DE-627)SPR054276055 (SPR)s00521-023-09069-9-e DE-627 ger DE-627 rakwb eng Lu, Tongwei verfasserin (orcid)0000-0002-3900-6456 aut Action recognition based on adaptive region perception 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. Temporal modelling (dpeaa)DE-He213 Adaptive areas (dpeaa)DE-He213 Long-range temporal information (dpeaa)DE-He213 Short-range temporal information (dpeaa)DE-He213 Yang, Qi aut Min, Feng aut Zhang, Yanduo aut Enthalten in Neural computing & applications London : Springer, 1993 36(2023), 2 vom: 09. Okt., Seite 943-959 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2023 number:2 day:09 month:10 pages:943-959 https://dx.doi.org/10.1007/s00521-023-09069-9 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 36 2023 2 09 10 943-959
allfieldsSound	10.1007/s00521-023-09069-9 doi (DE-627)SPR054276055 (SPR)s00521-023-09069-9-e DE-627 ger DE-627 rakwb eng Lu, Tongwei verfasserin (orcid)0000-0002-3900-6456 aut Action recognition based on adaptive region perception 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. Temporal modelling (dpeaa)DE-He213 Adaptive areas (dpeaa)DE-He213 Long-range temporal information (dpeaa)DE-He213 Short-range temporal information (dpeaa)DE-He213 Yang, Qi aut Min, Feng aut Zhang, Yanduo aut Enthalten in Neural computing & applications London : Springer, 1993 36(2023), 2 vom: 09. Okt., Seite 943-959 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2023 number:2 day:09 month:10 pages:943-959 https://dx.doi.org/10.1007/s00521-023-09069-9 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 36 2023 2 09 10 943-959
language	English
source	Enthalten in Neural computing & applications 36(2023), 2 vom: 09. Okt., Seite 943-959 volume:36 year:2023 number:2 day:09 month:10 pages:943-959
sourceStr	Enthalten in Neural computing & applications 36(2023), 2 vom: 09. Okt., Seite 943-959 volume:36 year:2023 number:2 day:09 month:10 pages:943-959
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Temporal modelling Adaptive areas Long-range temporal information Short-range temporal information
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container_title	Neural computing & applications
authorswithroles_txt_mv	Lu, Tongwei @@aut@@ Yang, Qi @@aut@@ Min, Feng @@aut@@ Zhang, Yanduo @@aut@@
publishDateDaySort_date	2023-10-09T00:00:00Z
hierarchy_top_id	271595574
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language_de	englisch
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author	Lu, Tongwei
spellingShingle	Lu, Tongwei misc Temporal modelling misc Adaptive areas misc Long-range temporal information misc Short-range temporal information Action recognition based on adaptive region perception
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topic_title	Action recognition based on adaptive region perception Temporal modelling (dpeaa)DE-He213 Adaptive areas (dpeaa)DE-He213 Long-range temporal information (dpeaa)DE-He213 Short-range temporal information (dpeaa)DE-He213
topic	misc Temporal modelling misc Adaptive areas misc Long-range temporal information misc Short-range temporal information
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title	Action recognition based on adaptive region perception
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title_full	Action recognition based on adaptive region perception
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title_sort	action recognition based on adaptive region perception
title_auth	Action recognition based on adaptive region perception
abstract	Abstract Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. © 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. © 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 Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance. © 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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title_short	Action recognition based on adaptive region perception
url	https://dx.doi.org/10.1007/s00521-023-09069-9
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up_date	2024-07-04T00:49:44.783Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054276055</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240106064635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240106s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-023-09069-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054276055</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00521-023-09069-9-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">Lu, Tongwei</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3900-6456</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Action recognition based on adaptive region perception</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Temporal modelling is still challenging for action recognition in video. To alleviate this problem, this paper proposes a new video architecture, called Adaptive Region Aware (ARP) network. The network focuses on combining short-range temporal information with long-range temporal information to perform effective action recognition. The core of our ARP is the Movement and Spatial-Temporal module (MST), which is made up of two modules, movement information and Spatial-Temporal information. MST uses the idea of difference for short-range temporal information extraction and adaptive region sensing and temporal convolution for long-range temporal information extraction. To extract temporal information for the entire video, our MST module contains two branches. Specifically, for local temporal information, we use the difference in motion between successive frames to extract a fine-grained representation of the motion, thus obtaining short-range temporal information. For the global temporal information, we use adaptive region awareness for feature extraction of the whole video to enhance the representation of the model in the spatio-temporal domain, and use temporal convolution for modelling to obtain our long-range temporal information. We insert the MST module into ResNet-50 to build our ARP network and experiment on the Something V1, Something V2 and Kinetics-400 datasets with optimal performance.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Temporal modelling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive areas</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Long-range temporal information</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Short-range temporal information</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Qi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Min, Feng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Yanduo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">London : Springer, 1993</subfield><subfield code="g">36(2023), 2 vom: 09. 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