Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition
Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficult...
Ausführliche Beschreibung
Autor*in: |
Sheng, Weijie [verfasserIn] Li, Xinde [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 395, Seite 86-94 |
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Übergeordnetes Werk: |
volume:395 ; pages:86-94 |
DOI / URN: |
10.1016/j.neucom.2020.01.098 |
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Katalog-ID: |
ELV004124952 |
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245 | 1 | 0 | |a Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition |
264 | 1 | |c 2020 | |
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520 | |a Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. | ||
650 | 4 | |a Gait recognition | |
650 | 4 | |a Siamese denoising autoencoder | |
650 | 4 | |a Joints trajectories reconstruction | |
650 | 4 | |a Autoencoder with LSTM | |
650 | 4 | |a Skeleton-based gait recognition | |
700 | 1 | |a Li, Xinde |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Neurocomputing |d Amsterdam : Elsevier, 1989 |g 395, Seite 86-94 |h Online-Ressource |w (DE-627)271176008 |w (DE-600)1479006-3 |w (DE-576)078412358 |x 1872-8286 |7 nnns |
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2020 |
allfields |
10.1016/j.neucom.2020.01.098 doi (DE-627)ELV004124952 (ELSEVIER)S0925-2312(20)30156-9 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Sheng, Weijie verfasserin (orcid)0000-0002-4498-8351 aut Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. Gait recognition Siamese denoising autoencoder Joints trajectories reconstruction Autoencoder with LSTM Skeleton-based gait recognition Li, Xinde verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 395, Seite 86-94 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:395 pages:86-94 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 395 86-94 |
spelling |
10.1016/j.neucom.2020.01.098 doi (DE-627)ELV004124952 (ELSEVIER)S0925-2312(20)30156-9 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Sheng, Weijie verfasserin (orcid)0000-0002-4498-8351 aut Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. Gait recognition Siamese denoising autoencoder Joints trajectories reconstruction Autoencoder with LSTM Skeleton-based gait recognition Li, Xinde verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 395, Seite 86-94 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:395 pages:86-94 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 395 86-94 |
allfields_unstemmed |
10.1016/j.neucom.2020.01.098 doi (DE-627)ELV004124952 (ELSEVIER)S0925-2312(20)30156-9 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Sheng, Weijie verfasserin (orcid)0000-0002-4498-8351 aut Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. Gait recognition Siamese denoising autoencoder Joints trajectories reconstruction Autoencoder with LSTM Skeleton-based gait recognition Li, Xinde verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 395, Seite 86-94 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:395 pages:86-94 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 395 86-94 |
allfieldsGer |
10.1016/j.neucom.2020.01.098 doi (DE-627)ELV004124952 (ELSEVIER)S0925-2312(20)30156-9 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Sheng, Weijie verfasserin (orcid)0000-0002-4498-8351 aut Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. Gait recognition Siamese denoising autoencoder Joints trajectories reconstruction Autoencoder with LSTM Skeleton-based gait recognition Li, Xinde verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 395, Seite 86-94 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:395 pages:86-94 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 395 86-94 |
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10.1016/j.neucom.2020.01.098 doi (DE-627)ELV004124952 (ELSEVIER)S0925-2312(20)30156-9 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Sheng, Weijie verfasserin (orcid)0000-0002-4498-8351 aut Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. Gait recognition Siamese denoising autoencoder Joints trajectories reconstruction Autoencoder with LSTM Skeleton-based gait recognition Li, Xinde verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 395, Seite 86-94 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:395 pages:86-94 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 395 86-94 |
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Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition |
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Sheng, Weijie |
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Sheng, Weijie Li, Xinde |
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Sheng, Weijie |
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title_sort |
siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition |
title_auth |
Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition |
abstract |
Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. |
abstractGer |
Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. |
abstract_unstemmed |
Dynamics of body skeletons convey significant information for human gait recognition. However, it is inevitable that missing points, overlapping error, or confusion of left and right error will frequently occur during the process of skeleton estimation. Existing skeleton-based methods have difficulty in achieving satisfactory performance in gait recognition since they treat the noisy data and the normal data equally to the recognition process. In this paper, we propose a novel skeleton-based model called Siamese Denoising Autoencoder networks (Siamese DAE), which can automatically learn to remove position noise, recover missing skeleton points and correct outliers in joint trajectories. More precisely, we construct an encoder that compresses the characteristics of input trajectories into a latent space and a decoder that attempts to reconstruct more accurate skeleton trajectories from the latent feature. The corrected joint trajectories not only lead to higher discriminative power but also stronger generalization capability. Moreover, we design a Siamese structure to reduce intra-class variations and increase inter-class variations of the encoded features. Experiments demonstrate that our method enhances the robustness against inaccurate skeleton estimation and achieves substantial improvements over mainstream skeleton-based methods for gait recognition. |
collection_details |
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title_short |
Siamese denoising autoencoders for joints trajectories reconstruction and robust gait recognition |
remote_bool |
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Li, Xinde |
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doi_str |
10.1016/j.neucom.2020.01.098 |
up_date |
2024-07-06T21:56:03.872Z |
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