Fine-grained activity classification in assembly based on multi-visual modalities
Abstract Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stag...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Haodong [verfasserIn] Zendehdel, Niloofar [verfasserIn] Leu, Ming C. [verfasserIn] Yin, Zhaozheng [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 Science+Business Media, LLC, 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: Journal of intelligent manufacturing - Springer US, 1990, 35(2023), 5 vom: 08. Juni, Seite 2215-2233 |
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| Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:5 ; day:08 ; month:06 ; pages:2215-2233 |
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| DOI / URN: |
10.1007/s10845-023-02152-x |
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| Katalog-ID: |
SPR056035438 |
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| 520 | |a Abstract Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. | ||
| 650 | 4 | |a Fine-grained activity |7 (dpeaa)DE-He213 | |
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10.1007/s10845-023-02152-x doi (DE-627)SPR056035438 (SPR)s10845-023-02152-x-e DE-627 ger DE-627 rakwb eng 004 620 VZ 52.72 bkl Chen, Haodong verfasserin (orcid)0000-0003-2428-870X aut Fine-grained activity classification in assembly based on multi-visual modalities 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. Fine-grained activity (dpeaa)DE-He213 Activity classification (dpeaa)DE-He213 Assembly (dpeaa)DE-He213 Multi-visual modality (dpeaa)DE-He213 Zendehdel, Niloofar verfasserin aut Leu, Ming C. verfasserin aut Yin, Zhaozheng verfasserin aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 35(2023), 5 vom: 08. Juni, Seite 2215-2233 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:35 year:2023 number:5 day:08 month:06 pages:2215-2233 https://dx.doi.org/10.1007/s10845-023-02152-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 VZ AR 35 2023 5 08 06 2215-2233 |
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10.1007/s10845-023-02152-x doi (DE-627)SPR056035438 (SPR)s10845-023-02152-x-e DE-627 ger DE-627 rakwb eng 004 620 VZ 52.72 bkl Chen, Haodong verfasserin (orcid)0000-0003-2428-870X aut Fine-grained activity classification in assembly based on multi-visual modalities 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. Fine-grained activity (dpeaa)DE-He213 Activity classification (dpeaa)DE-He213 Assembly (dpeaa)DE-He213 Multi-visual modality (dpeaa)DE-He213 Zendehdel, Niloofar verfasserin aut Leu, Ming C. verfasserin aut Yin, Zhaozheng verfasserin aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 35(2023), 5 vom: 08. Juni, Seite 2215-2233 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:35 year:2023 number:5 day:08 month:06 pages:2215-2233 https://dx.doi.org/10.1007/s10845-023-02152-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 VZ AR 35 2023 5 08 06 2215-2233 |
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10.1007/s10845-023-02152-x doi (DE-627)SPR056035438 (SPR)s10845-023-02152-x-e DE-627 ger DE-627 rakwb eng 004 620 VZ 52.72 bkl Chen, Haodong verfasserin (orcid)0000-0003-2428-870X aut Fine-grained activity classification in assembly based on multi-visual modalities 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. Fine-grained activity (dpeaa)DE-He213 Activity classification (dpeaa)DE-He213 Assembly (dpeaa)DE-He213 Multi-visual modality (dpeaa)DE-He213 Zendehdel, Niloofar verfasserin aut Leu, Ming C. verfasserin aut Yin, Zhaozheng verfasserin aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 35(2023), 5 vom: 08. Juni, Seite 2215-2233 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:35 year:2023 number:5 day:08 month:06 pages:2215-2233 https://dx.doi.org/10.1007/s10845-023-02152-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 VZ AR 35 2023 5 08 06 2215-2233 |
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10.1007/s10845-023-02152-x doi (DE-627)SPR056035438 (SPR)s10845-023-02152-x-e DE-627 ger DE-627 rakwb eng 004 620 VZ 52.72 bkl Chen, Haodong verfasserin (orcid)0000-0003-2428-870X aut Fine-grained activity classification in assembly based on multi-visual modalities 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. Fine-grained activity (dpeaa)DE-He213 Activity classification (dpeaa)DE-He213 Assembly (dpeaa)DE-He213 Multi-visual modality (dpeaa)DE-He213 Zendehdel, Niloofar verfasserin aut Leu, Ming C. verfasserin aut Yin, Zhaozheng verfasserin aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 35(2023), 5 vom: 08. Juni, Seite 2215-2233 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:35 year:2023 number:5 day:08 month:06 pages:2215-2233 https://dx.doi.org/10.1007/s10845-023-02152-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 VZ AR 35 2023 5 08 06 2215-2233 |
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10.1007/s10845-023-02152-x doi (DE-627)SPR056035438 (SPR)s10845-023-02152-x-e DE-627 ger DE-627 rakwb eng 004 620 VZ 52.72 bkl Chen, Haodong verfasserin (orcid)0000-0003-2428-870X aut Fine-grained activity classification in assembly based on multi-visual modalities 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. Fine-grained activity (dpeaa)DE-He213 Activity classification (dpeaa)DE-He213 Assembly (dpeaa)DE-He213 Multi-visual modality (dpeaa)DE-He213 Zendehdel, Niloofar verfasserin aut Leu, Ming C. verfasserin aut Yin, Zhaozheng verfasserin aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 35(2023), 5 vom: 08. Juni, Seite 2215-2233 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:35 year:2023 number:5 day:08 month:06 pages:2215-2233 https://dx.doi.org/10.1007/s10845-023-02152-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 VZ AR 35 2023 5 08 06 2215-2233 |
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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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. 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Fine-grained activity classification in assembly based on multi-visual modalities |
| abstract |
Abstract Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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 Assembly activity recognition and prediction help to improve productivity, quality control, and safety measures in smart factories. This study aims to sense, recognize, and predict a worker's continuous fine-grained assembly activities in a manufacturing platform. We propose a two-stage network for workers' fine-grained activity classification by leveraging scene-level and temporal-level activity features. The first stage is a feature awareness block that extracts scene-level features from multi-visual modalities, including red–green–blue (RGB) and hand skeleton frames. We use the transfer learning method in the first stage and compare three different pre-trained feature extraction models. Then, we transmit the feature information from the first stage to the second stage to learn the temporal-level features of activities. The second stage consists of the Recurrent Neural Network (RNN) layers and a final classifier. We compare the performance of two different RNNs in the second stage, including the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The partial video observation method is used in the prediction of fine-grained activities. In the experiments using the trimmed activity videos, our model achieves an accuracy of > 99% on our dataset and > 98% on the public dataset UCF 101, outperforming the state-of-the-art models. The prediction model achieves an accuracy of > 97% in predicting activity labels using 50% of the onset activity video information. In the experiments using an untrimmed video with continuous assembly activities, we combine our recognition and prediction models and achieve an accuracy of > 91% in real time, surpassing the state-of-the-art models for the recognition of continuous assembly activities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 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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Fine-grained activity classification in assembly based on multi-visual modalities |
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Zendehdel, Niloofar Leu, Ming C. Yin, Zhaozheng |
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Zendehdel, Niloofar Leu, Ming C. Yin, Zhaozheng |
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10.1007/s10845-023-02152-x |
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2024-07-03T19:45:06.051Z |
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|
| score |
7.400589 |