The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Joint Distance Maps Based Action Recognition With Convolutional Neural Networks
Open accessAbstract
Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.
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Cites
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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
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
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Mining actionlet ensemble for action recognition with depth cameras
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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
Action recognition based on a bag of 3D points
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Self-citationChen, 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
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Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
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Cited by
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00572 · Cited by 972
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
Pareek, P., & Thakkar, A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09904-8 · Cited by 455
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
Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection
Liu, J., Li, Y., Song, S., Xing, J., Lan, C., & Zeng, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799968 · Cited by 62
Metadata
- Authors
- Li, C., Hou, Y., Wang, P., & Li, W.
- Venue
- IEEE Signal Processing Letters
- Published
- 2017-05
- Volume
- 24
- Issue
- 5
- Pages
- 624-628
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2761860076
- Cited by
- 283
- References
- 18
- FWCI
- 12.69
- Type
- journal article
- Language
- en
- Open access
- green
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