A two-branch deep learning with spatial and pose constraints for social group detection

Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity gro...
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Autor*in:	Lu, Xiaoyan [verfasserIn] Li, Xinde [verfasserIn] Hu, Chuanfei [verfasserIn] Deng, Jin [verfasserIn] Sheng, Weijie [verfasserIn] Zhu, Lianli [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Video surveillance analysis Social group detection Spatial configuration Pose consistency

Übergeordnetes Werk:	Enthalten in: Engineering applications of artificial intelligence - Amsterdam [u.a.] : Elsevier Science, 1988, 124
Übergeordnetes Werk:	volume:124

DOI / URN:	10.1016/j.engappai.2023.106583

Katalog-ID:	ELV061392979

Internformat


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245	1	0	\|a A two-branch deep learning with spatial and pose constraints for social group detection
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520			\|a Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets.
650		4	\|a Video surveillance analysis
650		4	\|a Social group detection
650		4	\|a Spatial configuration
650		4	\|a Pose consistency
700	1		\|a Li, Xinde \|e verfasserin \|4 aut
700	1		\|a Hu, Chuanfei \|e verfasserin \|4 aut
700	1		\|a Deng, Jin \|e verfasserin \|0 (orcid)0000-0003-0017-9808 \|4 aut
700	1		\|a Sheng, Weijie \|e verfasserin \|4 aut
700	1		\|a Zhu, Lianli \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Engineering applications of artificial intelligence \|d Amsterdam [u.a.] : Elsevier Science, 1988 \|g 124 \|h Online-Ressource \|w (DE-627)308447832 \|w (DE-600)1502275-4 \|w (DE-576)094752524 \|x 0952-1976 \|7 nnns
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author_variant	x l xl x l xl c h ch j d jd w s ws l z lz
matchkey_str	article:09521976:2023----::tornheperigihptaadoeosritfr
hierarchy_sort_str	2023
bklnumber	50.23 54.72
publishDate	2023
allfields	10.1016/j.engappai.2023.106583 doi (DE-627)ELV061392979 (ELSEVIER)S0952-1976(23)00767-4 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Lu, Xiaoyan verfasserin (orcid)0000-0001-6117-1979 aut A two-branch deep learning with spatial and pose constraints for social group detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets. Video surveillance analysis Social group detection Spatial configuration Pose consistency Li, Xinde verfasserin aut Hu, Chuanfei verfasserin aut Deng, Jin verfasserin (orcid)0000-0003-0017-9808 aut Sheng, Weijie verfasserin aut Zhu, Lianli verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 124 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:124 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 124
spelling	10.1016/j.engappai.2023.106583 doi (DE-627)ELV061392979 (ELSEVIER)S0952-1976(23)00767-4 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Lu, Xiaoyan verfasserin (orcid)0000-0001-6117-1979 aut A two-branch deep learning with spatial and pose constraints for social group detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets. Video surveillance analysis Social group detection Spatial configuration Pose consistency Li, Xinde verfasserin aut Hu, Chuanfei verfasserin aut Deng, Jin verfasserin (orcid)0000-0003-0017-9808 aut Sheng, Weijie verfasserin aut Zhu, Lianli verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 124 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:124 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 124
allfields_unstemmed	10.1016/j.engappai.2023.106583 doi (DE-627)ELV061392979 (ELSEVIER)S0952-1976(23)00767-4 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Lu, Xiaoyan verfasserin (orcid)0000-0001-6117-1979 aut A two-branch deep learning with spatial and pose constraints for social group detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets. Video surveillance analysis Social group detection Spatial configuration Pose consistency Li, Xinde verfasserin aut Hu, Chuanfei verfasserin aut Deng, Jin verfasserin (orcid)0000-0003-0017-9808 aut Sheng, Weijie verfasserin aut Zhu, Lianli verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 124 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:124 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 124
allfieldsGer	10.1016/j.engappai.2023.106583 doi (DE-627)ELV061392979 (ELSEVIER)S0952-1976(23)00767-4 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Lu, Xiaoyan verfasserin (orcid)0000-0001-6117-1979 aut A two-branch deep learning with spatial and pose constraints for social group detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets. Video surveillance analysis Social group detection Spatial configuration Pose consistency Li, Xinde verfasserin aut Hu, Chuanfei verfasserin aut Deng, Jin verfasserin (orcid)0000-0003-0017-9808 aut Sheng, Weijie verfasserin aut Zhu, Lianli verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 124 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:124 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 124
allfieldsSound	10.1016/j.engappai.2023.106583 doi (DE-627)ELV061392979 (ELSEVIER)S0952-1976(23)00767-4 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Lu, Xiaoyan verfasserin (orcid)0000-0001-6117-1979 aut A two-branch deep learning with spatial and pose constraints for social group detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets. Video surveillance analysis Social group detection Spatial configuration Pose consistency Li, Xinde verfasserin aut Hu, Chuanfei verfasserin aut Deng, Jin verfasserin (orcid)0000-0003-0017-9808 aut Sheng, Weijie verfasserin aut Zhu, Lianli verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 124 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:124 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 124
language	English
source	Enthalten in Engineering applications of artificial intelligence 124 volume:124
sourceStr	Enthalten in Engineering applications of artificial intelligence 124 volume:124
format_phy_str_mv	Article
bklname	Regelungstechnik Steuerungstechnik Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Video surveillance analysis Social group detection Spatial configuration Pose consistency
dewey-raw	004
isfreeaccess_bool	false
container_title	Engineering applications of artificial intelligence
authorswithroles_txt_mv	Lu, Xiaoyan @@aut@@ Li, Xinde @@aut@@ Hu, Chuanfei @@aut@@ Deng, Jin @@aut@@ Sheng, Weijie @@aut@@ Zhu, Lianli @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	308447832
dewey-sort	14
id	ELV061392979
language_de	englisch
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author	Lu, Xiaoyan
spellingShingle	Lu, Xiaoyan ddc 004 bkl 50.23 bkl 54.72 misc Video surveillance analysis misc Social group detection misc Spatial configuration misc Pose consistency A two-branch deep learning with spatial and pose constraints for social group detection
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topic_title	004 VZ 50.23 bkl 54.72 bkl A two-branch deep learning with spatial and pose constraints for social group detection Video surveillance analysis Social group detection Spatial configuration Pose consistency
topic	ddc 004 bkl 50.23 bkl 54.72 misc Video surveillance analysis misc Social group detection misc Spatial configuration misc Pose consistency
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title	A two-branch deep learning with spatial and pose constraints for social group detection
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title_full	A two-branch deep learning with spatial and pose constraints for social group detection
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title_sort	a two-branch deep learning with spatial and pose constraints for social group detection
title_auth	A two-branch deep learning with spatial and pose constraints for social group detection
abstract	Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets.
abstractGer	Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets.
abstract_unstemmed	Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T = 1 ) on four public datasets.
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title_short	A two-branch deep learning with spatial and pose constraints for social group detection
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author2	Li, Xinde Hu, Chuanfei Deng, Jin Sheng, Weijie Zhu, Lianli
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up_date	2024-07-06T17:35:53.994Z
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A two-branch deep learning with spatial and pose constraints for social group detection

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