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RGB-D action recognition based on discriminative common structure learning model.

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Title: RGB-D action recognition based on discriminative common structure learning model.
Authors: Liu, Tianshan1, Kong, Jun1 kongjun@jiangnan.edu.cn, Jiang, Min1, Huo, Hongtao2
Source: Journal of Electronic Imaging. Mar/Apr2019, Vol. 28 Issue 2, p1-15. 15p.
Subjects: Information commons, Human activity recognition, Learning, Optical engineering, Human behavior
Abstract: The emergence of low-cost depth cameras creates potential for RGB-D based human action recognition. However, most of the existing RGB-D based approaches simply concatenate original heterogeneous features without discovering the latent relations among different modalities. We propose a discriminative common structure learning (DCSL) model for human action recognition from RGB-D sequences. Specifically, we extract deep learning-based features and hand-crafted features from multimodal data (skeleton, depth, and RGB). In particular, we propose a deep architecture based on 3-D convolutional neural network to automatically extract deep spatiotemporal features from raw sequences. The proposed DCSL model utilizes a generalized version of collective matrix factorization to learn shared features among different modalities. To perform supervised learning and preserve intermodal similarity, we formulate a graph regularization term by considering both label information and similar geometric structure of multimodal data, which intends to improve the discriminative power of shared features. Moreover, we solve the objective function using an iterative optimization algorithm. Then, an improved collaborative representation classifier is employed to perform computationally efficient action recognition. Experimental results on four action datasets demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: The emergence of low-cost depth cameras creates potential for RGB-D based human action recognition. However, most of the existing RGB-D based approaches simply concatenate original heterogeneous features without discovering the latent relations among different modalities. We propose a discriminative common structure learning (DCSL) model for human action recognition from RGB-D sequences. Specifically, we extract deep learning-based features and hand-crafted features from multimodal data (skeleton, depth, and RGB). In particular, we propose a deep architecture based on 3-D convolutional neural network to automatically extract deep spatiotemporal features from raw sequences. The proposed DCSL model utilizes a generalized version of collective matrix factorization to learn shared features among different modalities. To perform supervised learning and preserve intermodal similarity, we formulate a graph regularization term by considering both label information and similar geometric structure of multimodal data, which intends to improve the discriminative power of shared features. Moreover, we solve the objective function using an iterative optimization algorithm. Then, an improved collaborative representation classifier is employed to perform computationally efficient action recognition. Experimental results on four action datasets demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1117/1.JEI.28.2.023012
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 15
        StartPage: 1
    Subjects:
      – SubjectFull: Information commons
        Type: general
      – SubjectFull: Human activity recognition
        Type: general
      – SubjectFull: Learning
        Type: general
      – SubjectFull: Optical engineering
        Type: general
      – SubjectFull: Human behavior
        Type: general
    Titles:
      – TitleFull: RGB-D action recognition based on discriminative common structure learning model.
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            NameFull: Liu, Tianshan
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            NameFull: Kong, Jun
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            NameFull: Jiang, Min
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            NameFull: Huo, Hongtao
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          Dates:
            – D: 01
              M: 03
              Text: Mar/Apr2019
              Type: published
              Y: 2019
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              Value: 28
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            – TitleFull: Journal of Electronic Imaging
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