The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Open accessAbstract
Single modality action recognition on RGB or depth sequences has been extensively explored recently. It is generally accepted that each of these two modalities has different strengths and limitations for the task of action recognition. Therefore, analysis of the RGB+D videos can help us to better study the complementary properties of these two types of modalities and achieve higher levels of performance. In this paper, we propose a new deep autoencoder based shared-specific feature factorization network to separate input multimodal signals into a hierarchy of components. Further, based on the structure of the features, a structured sparsity learning machine is proposed which utilizes mixed norms to apply regularization within components and group selection between them for better classification performance. Our experimental results show the effectiveness of our cross-modality feature analysis framework by achieving state-of-the-art accuracy for action classification on five challenging benchmark datasets.
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Cited by
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148
Self-citationHuman Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
Self-citation2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Luvizon, D. C., Picard, D., & Tabia, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00539 · Cited by 555
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
Liu, J., Wang, G., Duan, L.-Y., Abdiyeva, K., & Kot, A. C. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2785279 · Cited by 537
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Metadata
- Authors
- Shahroudy, A., Ng, T.-T., Gong, Y., & Wang, G.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2018-05-01
- Volume
- 40
- Issue
- 5
- Pages
- 1045-1058
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2309561466
- Cited by
- 264
- References
- 12
- FWCI
- 14.38
- Type
- journal article
- Language
- en
- Open access
- green
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