EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification

Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Minling Zhu [verfasserIn] Huimin Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	person re-identification occluded deformable attention symmetry

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 15(2023), 4, p 906
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:4, p 906

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym15040906

Katalog-ID:	DOAJ089762932

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spelling	10.3390/sym15040906 doi (DE-627)DOAJ089762932 (DE-599)DOAJ3f4214c82ca84a988e5b10e3aeea5e2d DE-627 ger DE-627 rakwb eng QA1-939 Minling Zhu verfasserin aut EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks. person re-identification occluded deformable attention symmetry Mathematics Huimin Zhou verfasserin aut In Symmetry MDPI AG, 2009 15(2023), 4, p 906 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:15 year:2023 number:4, p 906 https://doi.org/10.3390/sym15040906 kostenfrei https://doaj.org/article/3f4214c82ca84a988e5b10e3aeea5e2d kostenfrei https://www.mdpi.com/2073-8994/15/4/906 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 4, p 906
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callnumber-first	Q - Science
author	Minling Zhu
spellingShingle	Minling Zhu misc QA1-939 misc person re-identification misc occluded misc deformable misc attention misc symmetry misc Mathematics EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification
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topic_title	QA1-939 EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification person re-identification occluded deformable attention symmetry
topic	misc QA1-939 misc person re-identification misc occluded misc deformable misc attention misc symmetry misc Mathematics
topic_unstemmed	misc QA1-939 misc person re-identification misc occluded misc deformable misc attention misc symmetry misc Mathematics
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title	EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification
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title_sort	ecreid: enhancing correlations from skeleton for occluded person re-identification
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title_auth	EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification
abstract	Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks.
abstractGer	Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks.
abstract_unstemmed	Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks.
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title_short	EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification
url	https://doi.org/10.3390/sym15040906 https://doaj.org/article/3f4214c82ca84a988e5b10e3aeea5e2d https://www.mdpi.com/2073-8994/15/4/906 https://doaj.org/toc/2073-8994
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It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. 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