Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture
Abstract Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard vis...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bose, S. Rubin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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: SN Computer Science - Singapore : Springer Singapore, 2020, 5(2024), 2 vom: 20. Jan. |
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| Übergeordnetes Werk: |
volume:5 ; year:2024 ; number:2 ; day:20 ; month:01 |
| Links: |
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| DOI / URN: |
10.1007/s42979-023-02474-w |
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| Katalog-ID: |
SPR054464633 |
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| 520 | |a Abstract Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. | ||
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| 700 | 1 | |a Kumar, V. Sathiesh |4 aut | |
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10.1007/s42979-023-02474-w doi (DE-627)SPR054464633 (SPR)s42979-023-02474-w-e DE-627 ger DE-627 rakwb eng Bose, S. Rubin verfasserin (orcid)0000-0001-5560-8580 aut Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. Single Stage Detector (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Hand gesture Recog- nition (dpeaa)DE-He213 Hand Detection (dpeaa)DE-He213 Yolo-v2 (dpeaa)DE-He213 Yolo-v3 (dpeaa)DE-He213 Kumar, V. Sathiesh aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 20. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:20 month:01 https://dx.doi.org/10.1007/s42979-023-02474-w 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_105 GBV_ILN_110 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 5 2024 2 20 01 |
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10.1007/s42979-023-02474-w doi (DE-627)SPR054464633 (SPR)s42979-023-02474-w-e DE-627 ger DE-627 rakwb eng Bose, S. Rubin verfasserin (orcid)0000-0001-5560-8580 aut Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. Single Stage Detector (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Hand gesture Recog- nition (dpeaa)DE-He213 Hand Detection (dpeaa)DE-He213 Yolo-v2 (dpeaa)DE-He213 Yolo-v3 (dpeaa)DE-He213 Kumar, V. Sathiesh aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 20. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:20 month:01 https://dx.doi.org/10.1007/s42979-023-02474-w 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_105 GBV_ILN_110 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 5 2024 2 20 01 |
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10.1007/s42979-023-02474-w doi (DE-627)SPR054464633 (SPR)s42979-023-02474-w-e DE-627 ger DE-627 rakwb eng Bose, S. Rubin verfasserin (orcid)0000-0001-5560-8580 aut Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. Single Stage Detector (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Hand gesture Recog- nition (dpeaa)DE-He213 Hand Detection (dpeaa)DE-He213 Yolo-v2 (dpeaa)DE-He213 Yolo-v3 (dpeaa)DE-He213 Kumar, V. Sathiesh aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 20. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:20 month:01 https://dx.doi.org/10.1007/s42979-023-02474-w 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_105 GBV_ILN_110 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 5 2024 2 20 01 |
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10.1007/s42979-023-02474-w doi (DE-627)SPR054464633 (SPR)s42979-023-02474-w-e DE-627 ger DE-627 rakwb eng Bose, S. Rubin verfasserin (orcid)0000-0001-5560-8580 aut Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. Single Stage Detector (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Hand gesture Recog- nition (dpeaa)DE-He213 Hand Detection (dpeaa)DE-He213 Yolo-v2 (dpeaa)DE-He213 Yolo-v3 (dpeaa)DE-He213 Kumar, V. Sathiesh aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 20. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:20 month:01 https://dx.doi.org/10.1007/s42979-023-02474-w 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_105 GBV_ILN_110 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 5 2024 2 20 01 |
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10.1007/s42979-023-02474-w doi (DE-627)SPR054464633 (SPR)s42979-023-02474-w-e DE-627 ger DE-627 rakwb eng Bose, S. Rubin verfasserin (orcid)0000-0001-5560-8580 aut Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. Single Stage Detector (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Hand gesture Recog- nition (dpeaa)DE-He213 Hand Detection (dpeaa)DE-He213 Yolo-v2 (dpeaa)DE-He213 Yolo-v3 (dpeaa)DE-He213 Kumar, V. Sathiesh aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 20. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:20 month:01 https://dx.doi.org/10.1007/s42979-023-02474-w 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_105 GBV_ILN_110 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 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 5 2024 2 20 01 |
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Bose, S. Rubin |
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Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture Single Stage Detector (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 CNN (dpeaa)DE-He213 Hand gesture Recog- nition (dpeaa)DE-He213 Hand Detection (dpeaa)DE-He213 Yolo-v2 (dpeaa)DE-He213 Yolo-v3 (dpeaa)DE-He213 |
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precise hand gesture recognition under hard visual environments using deep architecture |
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Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture |
| abstract |
Abstract Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 Hand gesture recognition is a way of capturing and translating the human signs into commands utilizing a visual interface. In this paper, You Only Look Once (Yolo) based deep single-stage convolutional neural network (CNN) is proposed for real-time multi-hand sign recognition under hard visual environments. The Yolo-v2 and Yolo-v3 models are utilized for real-time hand sign recognition. The DarkNet-19 and DarkNet-53 CNN architectures are used as a backbone networks in Yolo-v2 and Yolo-v3 respectively. Three distinct datasets (NUSHP-II, SENZ-3D, and MITI-HD) are used to train and validate the models. The models are evaluated using a test dataset with an IoU range of 0.5 to 0.95. The Yolo-v2 CNN model achieved an average precision value of 99.10% for AP0.5, 93.00% for AP0.75, and 78.30% for AP0.5:0.95 on the MITI-Hand dataset. The Yolo-v3 CNN model achieved an average precision value of 99.18% for AP0.5. The Yolo model with Adam Optimizer performs better com- pared to other optimization approaches. The prediction time obtained by using the Yolo-v2 and Yolo-v3 CNN models is 20 ms and 25 ms, respectively. The proposed Yolo-v3 CNN model efficiently recognizes the multi-hand signs under hard visual environments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 |
Precise Hand Gesture Recognition under Hard Visual Environments using Deep Architecture |
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https://dx.doi.org/10.1007/s42979-023-02474-w |
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Kumar, V. Sathiesh |
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| doi_str |
10.1007/s42979-023-02474-w |
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2024-07-04T01:44:54.750Z |
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| score |
7.399021 |