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Cited by 257

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Spatio-Temporal Attention-Based LSTM Networks for 3D Action Recognition and Detection

Song, Sijie, Lan, Cuiling, Xing, Junliang, Zeng, Wenjun, Liu, Jiaying·2018·IEEE Transactions on Image Processing
DOI 10.1109/tip.2018.2818328
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Abstract

Human action analytics has attracted a lot of attention for decades in computer vision. It is important to extract discriminative spatio-temporal features to model the spatial and temporal evolutions of different actions. In this paper, we propose a spatial and temporal attention model to explore the spatial and temporal discriminative features for human action recognition and detection from skeleton data. We build our networks based on the recurrent neural networks with long short-term memory units. The learned model is capable of selectively focusing on discriminative joints of skeletons within each input frame and paying different levels of attention to the outputs of different frames. To ensure effective training of the network for action recognition, we propose a regularized cross-entropy loss to drive the learning process and develop a joint training strategy accordingly. Moreover, based on temporal attention, we develop a method to generate the action temporal proposals for action detection. We evaluate the proposed method on the SBU Kinect Interaction data set, the NTU RGB + D data set, and the PKU-MMD data set, respectively. Experiment results demonstrate the effectiveness of our proposed model on both action recognition and action detection.

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Research works printed but not linked: 58

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Cited by

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Metadata

Authors
Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J.
Venue
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018-07
Volume
27
Issue
7
Pages
3459-3471
ISSN
  • 1057-7149 (ISSN-L)
  • 1941-0042
DOI
10.1109/tip.2018.2818328
OpenAlex
W2792140610
Cited by
257
References
7
FWCI
13.21
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Funders
  • National Natural Science Foundation of China (61772043, 61672519)
  • Microsoft Research Asia Fund (FY17-RES-THEME-013)
  • CCF-Tencent Open Research Fund

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