Contactless interaction recognition and interactor detection in multi-person scenes
Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challengin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Jiacheng [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: |
© Higher Education Press 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Frontiers of computer science in China - Beijing : Higher Education Press, 2007, 18(2023), 5 vom: 23. Dez. |
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| Übergeordnetes Werk: |
volume:18 ; year:2023 ; number:5 ; day:23 ; month:12 |
| Links: |
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| DOI / URN: |
10.1007/s11704-023-2418-0 |
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| 520 | |a Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. | ||
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| 700 | 1 | |a Wang, Song |4 aut | |
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10.1007/s11704-023-2418-0 doi (DE-627)SPR054165024 (SPR)s11704-023-2418-0-e DE-627 ger DE-627 rakwb eng Li, Jiacheng verfasserin aut Contactless interaction recognition and interactor detection in multi-person scenes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2024 Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. human-human interaction recognition (dpeaa)DE-He213 multiperson scene (dpeaa)DE-He213 contactless interaction (dpeaa)DE-He213 human relation modeling (dpeaa)DE-He213 Han, Ruize aut Feng, Wei aut Yan, Haomin aut Wang, Song aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 18(2023), 5 vom: 23. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:18 year:2023 number:5 day:23 month:12 https://dx.doi.org/10.1007/s11704-023-2418-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 18 2023 5 23 12 |
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10.1007/s11704-023-2418-0 doi (DE-627)SPR054165024 (SPR)s11704-023-2418-0-e DE-627 ger DE-627 rakwb eng Li, Jiacheng verfasserin aut Contactless interaction recognition and interactor detection in multi-person scenes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2024 Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. human-human interaction recognition (dpeaa)DE-He213 multiperson scene (dpeaa)DE-He213 contactless interaction (dpeaa)DE-He213 human relation modeling (dpeaa)DE-He213 Han, Ruize aut Feng, Wei aut Yan, Haomin aut Wang, Song aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 18(2023), 5 vom: 23. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:18 year:2023 number:5 day:23 month:12 https://dx.doi.org/10.1007/s11704-023-2418-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 18 2023 5 23 12 |
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10.1007/s11704-023-2418-0 doi (DE-627)SPR054165024 (SPR)s11704-023-2418-0-e DE-627 ger DE-627 rakwb eng Li, Jiacheng verfasserin aut Contactless interaction recognition and interactor detection in multi-person scenes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2024 Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. human-human interaction recognition (dpeaa)DE-He213 multiperson scene (dpeaa)DE-He213 contactless interaction (dpeaa)DE-He213 human relation modeling (dpeaa)DE-He213 Han, Ruize aut Feng, Wei aut Yan, Haomin aut Wang, Song aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 18(2023), 5 vom: 23. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:18 year:2023 number:5 day:23 month:12 https://dx.doi.org/10.1007/s11704-023-2418-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 18 2023 5 23 12 |
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10.1007/s11704-023-2418-0 doi (DE-627)SPR054165024 (SPR)s11704-023-2418-0-e DE-627 ger DE-627 rakwb eng Li, Jiacheng verfasserin aut Contactless interaction recognition and interactor detection in multi-person scenes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2024 Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. human-human interaction recognition (dpeaa)DE-He213 multiperson scene (dpeaa)DE-He213 contactless interaction (dpeaa)DE-He213 human relation modeling (dpeaa)DE-He213 Han, Ruize aut Feng, Wei aut Yan, Haomin aut Wang, Song aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 18(2023), 5 vom: 23. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:18 year:2023 number:5 day:23 month:12 https://dx.doi.org/10.1007/s11704-023-2418-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 18 2023 5 23 12 |
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10.1007/s11704-023-2418-0 doi (DE-627)SPR054165024 (SPR)s11704-023-2418-0-e DE-627 ger DE-627 rakwb eng Li, Jiacheng verfasserin aut Contactless interaction recognition and interactor detection in multi-person scenes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2024 Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. human-human interaction recognition (dpeaa)DE-He213 multiperson scene (dpeaa)DE-He213 contactless interaction (dpeaa)DE-He213 human relation modeling (dpeaa)DE-He213 Han, Ruize aut Feng, Wei aut Yan, Haomin aut Wang, Song aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 18(2023), 5 vom: 23. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:18 year:2023 number:5 day:23 month:12 https://dx.doi.org/10.1007/s11704-023-2418-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 18 2023 5 23 12 |
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Li, Jiacheng misc human-human interaction recognition misc multiperson scene misc contactless interaction misc human relation modeling Contactless interaction recognition and interactor detection in multi-person scenes |
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Contactless interaction recognition and interactor detection in multi-person scenes human-human interaction recognition (dpeaa)DE-He213 multiperson scene (dpeaa)DE-He213 contactless interaction (dpeaa)DE-He213 human relation modeling (dpeaa)DE-He213 |
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Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. © Higher Education Press 2024 |
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Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. © Higher Education Press 2024 |
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Abstract Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. © Higher Education Press 2024 |
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