Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture

Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling...
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Autor*in:	Jianfang Li [verfasserIn] Degui Xiao [verfasserIn] Keqin Li [verfasserIn] Jiazhi Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Data preprocessing graph matching LSTM MOCAP data

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 34868-34881
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:34868-34881

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3060385

Katalog-ID:	DOAJ062677063

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520			\|a Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.
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spelling	10.1109/ACCESS.2021.3060385 doi (DE-627)DOAJ062677063 (DE-599)DOAJ0903816d0c6d4898b88d89b52466a0da DE-627 ger DE-627 rakwb eng TK1-9971 Jianfang Li verfasserin aut Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling. Data preprocessing graph matching LSTM MOCAP data Electrical engineering. Electronics. Nuclear engineering Degui Xiao verfasserin aut Keqin Li verfasserin aut Jiazhi Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 34868-34881 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:34868-34881 https://doi.org/10.1109/ACCESS.2021.3060385 kostenfrei https://doaj.org/article/0903816d0c6d4898b88d89b52466a0da kostenfrei https://ieeexplore.ieee.org/document/9358157/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 34868-34881
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allfieldsGer	10.1109/ACCESS.2021.3060385 doi (DE-627)DOAJ062677063 (DE-599)DOAJ0903816d0c6d4898b88d89b52466a0da DE-627 ger DE-627 rakwb eng TK1-9971 Jianfang Li verfasserin aut Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling. Data preprocessing graph matching LSTM MOCAP data Electrical engineering. Electronics. Nuclear engineering Degui Xiao verfasserin aut Keqin Li verfasserin aut Jiazhi Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 34868-34881 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:34868-34881 https://doi.org/10.1109/ACCESS.2021.3060385 kostenfrei https://doaj.org/article/0903816d0c6d4898b88d89b52466a0da kostenfrei https://ieeexplore.ieee.org/document/9358157/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 34868-34881
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callnumber-first	T - Technology
author	Jianfang Li
spellingShingle	Jianfang Li misc TK1-9971 misc Data preprocessing misc graph matching misc LSTM misc MOCAP data misc Electrical engineering. Electronics. Nuclear engineering Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture
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topic_title	TK1-9971 Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture Data preprocessing graph matching LSTM MOCAP data
topic	misc TK1-9971 misc Data preprocessing misc graph matching misc LSTM misc MOCAP data misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Data preprocessing misc graph matching misc LSTM misc MOCAP data misc Electrical engineering. Electronics. Nuclear engineering
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title	Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture
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title_full	Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture
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title_sort	graph matching for marker labeling and missing marker reconstruction with bone constraint by lstm in optical motion capture
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title_auth	Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture
abstract	Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.
abstractGer	Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.
abstract_unstemmed	Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.
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title_short	Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture
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