The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks
Abstract
This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum representation makes it possible to use a standard ConvNet architecture to learn suitable “dynamic” features from skeleton sequences without training millions of parameters afresh and it is especially valuable when there is insufficient annotated training video data. Specifically, the encoding consists of four steps: mapping of joint distribution, spectrum coding of joint trajectories, spectrum coding of body parts, and joint velocity weighted saturation and brightness. Experimental results on three widely used datasets have demonstrated the efficacy of the proposed method.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995316 · Cited by 3,491
Hierarchical recurrent neural network for skeleton based action recognition
Du, Y., Wang, W., & Wang, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298714 · Cited by 1,895
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
Vemulapalli, R., Arrate, F., & Chellappa, R. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.82 · Cited by 1,517
Mining actionlet ensemble for action recognition with depth cameras
Wang, J., Liu, Z., Wu, Y., & Yuan, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247813 · Cited by 1,512
View invariant human action recognition using histograms of 3D joints
Xia, L., Chen, C.-C., & Aggarwal, J. K. (2012) · 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239233 · Cited by 1,452
Chen, C., Jafari, R., & Kehtarnavaz, N. (2015) · 2015 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2015.7350781 · Cited by 765
Differential Recurrent Neural Networks for Action Recognition
Veeriah, V., Zhuang, N., & Qi, G.-J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.460 · Cited by 516
Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations
Hussein, M. E., Torki, M., Gowayyed, M. A., & El-Saban, M. (2013) · Cited by 507
EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor
Yang, X., & Tian, Y. L. (2012) · 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239232 · Cited by 439
Cited by
Human 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
Recognizing Human Actions as the Evolution of Pose Estimation Maps
Liu, M., & Yuan, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00127 · Cited by 336
Joint Distance Maps Based Action Recognition With Convolutional Neural Networks
Li, C., Hou, Y., Wang, P., & Li, W. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2678539 · Cited by 283
Self-citationSpatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
Shu, X., Zhang, L., Qi, G.-J., Liu, W., & Tang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050918 · Cited by 276
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition
Wu, W., Hua, Y., Zheng, C., Wu, S., Chen, C., & Lu, A. (2023) · 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) · doi:10.1109/icmew59549.2023.00045 · Cited by 60
Metadata
- Authors
- Hou, Y., Li, Z., Wang, P., & Li, W.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2018-03
- Volume
- 28
- Issue
- 3
- Pages
- 807-811
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2554408731
- Cited by
- 339
- References
- 23
- FWCI
- 22.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hou, Y., Li, Z., Wang, P., & Li, W. (2018). Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2016.2628339
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