View-invariant gait recognition based on kinect skeleton feature
Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, whi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Jiande [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 77(2018), 19 vom: 22. Feb., Seite 24909-24935 |
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| Übergeordnetes Werk: |
volume:77 ; year:2018 ; number:19 ; day:22 ; month:02 ; pages:24909-24935 |
| Links: |
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| DOI / URN: |
10.1007/s11042-018-5722-1 |
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OLC2035053137 |
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| 520 | |a Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. | ||
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| 650 | 4 | |a View-Invariant | |
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| 700 | 1 | |a Wang, Yufei |4 aut | |
| 700 | 1 | |a Li, Jing |4 aut | |
| 700 | 1 | |a Wan, Wenbo |4 aut | |
| 700 | 1 | |a Cheng, De |4 aut | |
| 700 | 1 | |a Zhang, Huaxiang |4 aut | |
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10.1007/s11042-018-5722-1 doi (DE-627)OLC2035053137 (DE-He213)s11042-018-5722-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sun, Jiande verfasserin aut View-invariant gait recognition based on kinect skeleton feature 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. Gait Recognition Second Generation Kinect View-Invariant 3D Joint Information Gait Dataset Wang, Yufei aut Li, Jing aut Wan, Wenbo aut Cheng, De aut Zhang, Huaxiang aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2018), 19 vom: 22. Feb., Seite 24909-24935 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2018 number:19 day:22 month:02 pages:24909-24935 https://doi.org/10.1007/s11042-018-5722-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2018 19 22 02 24909-24935 |
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10.1007/s11042-018-5722-1 doi (DE-627)OLC2035053137 (DE-He213)s11042-018-5722-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sun, Jiande verfasserin aut View-invariant gait recognition based on kinect skeleton feature 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. Gait Recognition Second Generation Kinect View-Invariant 3D Joint Information Gait Dataset Wang, Yufei aut Li, Jing aut Wan, Wenbo aut Cheng, De aut Zhang, Huaxiang aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2018), 19 vom: 22. Feb., Seite 24909-24935 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2018 number:19 day:22 month:02 pages:24909-24935 https://doi.org/10.1007/s11042-018-5722-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2018 19 22 02 24909-24935 |
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10.1007/s11042-018-5722-1 doi (DE-627)OLC2035053137 (DE-He213)s11042-018-5722-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sun, Jiande verfasserin aut View-invariant gait recognition based on kinect skeleton feature 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. Gait Recognition Second Generation Kinect View-Invariant 3D Joint Information Gait Dataset Wang, Yufei aut Li, Jing aut Wan, Wenbo aut Cheng, De aut Zhang, Huaxiang aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2018), 19 vom: 22. Feb., Seite 24909-24935 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2018 number:19 day:22 month:02 pages:24909-24935 https://doi.org/10.1007/s11042-018-5722-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2018 19 22 02 24909-24935 |
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10.1007/s11042-018-5722-1 doi (DE-627)OLC2035053137 (DE-He213)s11042-018-5722-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sun, Jiande verfasserin aut View-invariant gait recognition based on kinect skeleton feature 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. Gait Recognition Second Generation Kinect View-Invariant 3D Joint Information Gait Dataset Wang, Yufei aut Li, Jing aut Wan, Wenbo aut Cheng, De aut Zhang, Huaxiang aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2018), 19 vom: 22. Feb., Seite 24909-24935 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2018 number:19 day:22 month:02 pages:24909-24935 https://doi.org/10.1007/s11042-018-5722-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2018 19 22 02 24909-24935 |
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10.1007/s11042-018-5722-1 doi (DE-627)OLC2035053137 (DE-He213)s11042-018-5722-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Sun, Jiande verfasserin aut View-invariant gait recognition based on kinect skeleton feature 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. Gait Recognition Second Generation Kinect View-Invariant 3D Joint Information Gait Dataset Wang, Yufei aut Li, Jing aut Wan, Wenbo aut Cheng, De aut Zhang, Huaxiang aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2018), 19 vom: 22. Feb., Seite 24909-24935 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2018 number:19 day:22 month:02 pages:24909-24935 https://doi.org/10.1007/s11042-018-5722-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2018 19 22 02 24909-24935 |
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View-invariant gait recognition based on kinect skeleton feature |
| abstract |
Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 |
| container_issue |
19 |
| title_short |
View-invariant gait recognition based on kinect skeleton feature |
| url |
https://doi.org/10.1007/s11042-018-5722-1 |
| remote_bool |
false |
| author2 |
Wang, Yufei Li, Jing Wan, Wenbo Cheng, De Zhang, Huaxiang |
| author2Str |
Wang, Yufei Li, Jing Wan, Wenbo Cheng, De Zhang, Huaxiang |
| ppnlink |
189064145 |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s11042-018-5722-1 |
| up_date |
2024-07-03T23:36:58.612Z |
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1803602958600896512 |
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7.401497 |