The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Open accessAbstract
Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.
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Cites
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LSTM neural networks for language modeling
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Unsupervised Learning of Video Representations using LSTMs
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Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133
P-CNN: Pose-Based CNN Features for Action Recognition
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A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
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“…(RNNs) which can handle the sequential data with variable lengths [10], [11], have shown their strength in language modeling [12], [13], [14], image captioning [15], [16], video analysis [17], [18], [19], [20], [21], [22], [23], [24], [25], and RGB-based activity recognition [26], [27], [28], [29].”
BackgroundWeakly Supervised Action Learning with RNN Based Fine-to-Coarse Modeling
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Cited by
LSTM-CNN Architecture for Human Activity Recognition
Xia, K., Huang, J., & Wang, H. (2020) · IEEE Access · doi:10.1109/access.2020.2982225 · Cited by 851
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
Self-citationSkeleton-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
Self-citationDeep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
Tang, Y., Tian, Y., Lu, J., Li, P., & Zhou, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00558 · Cited by 444
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Cheng, K., Zhang, Y., Cao, C., Shi, L., Cheng, J., & Lu, H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_32 · Cited by 384
Metadata
- Authors
- Liu, J., Shahroudy, A., Xu, D., Kot, A. C., & Wang, G.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2018-12-01
- Volume
- 40
- Issue
- 12
- Pages
- 3007-3021
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2716916105
- Cited by
- 530
- References
- 10
- FWCI
- 20.10
- Type
- journal article
- Language
- en
- Open access
- green
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