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Cited by 1,512

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Mining actionlet ensemble for action recognition with depth cameras

Open access
Jiang Wang, Zicheng Liu, Ying Wu, Junsong Yuan·2012·2012 IEEE Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2012.6247813
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Abstract

Human action recognition is an important yet challenging task. The recently developed commodity depth sensors open up new possibilities of dealing with this problem but also present some unique challenges. The depth maps captured by the depth cameras are very noisy and the 3D positions of the tracked joints may be completely wrong if serious occlusions occur, which increases the intra-class variations in the actions. In this paper, an actionlet ensemble model is learnt to represent each action and to capture the intra-class variance. In addition, novel features that are suitable for depth data are proposed. They are robust to noise, invariant to translational and temporal misalignments, and capable of characterizing both the human motion and the human-object interactions. The proposed approach is evaluated on two challenging action recognition datasets captured by commodity depth cameras, and another dataset captured by a MoCap system. The experimental evaluations show that the proposed approach achieves superior performance to the state of the art algorithms.

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Metadata

Authors
Wang, J., Liu, Z., Wu, Y., & Yuan, J.
Venue
2012 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2012-06
Pages
1290-1297
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2012.6247813
OpenAlex
W2143267104
Cited by
1,512
References
3
FWCI
85.47
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Mining actionlet ensemble for action recognition with depth cameras. https://app.cochat.ai/papers/10.1109/cvpr.2012.6247813
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Wang, J., Liu, Z., Wu, Y., & Yuan, J. (2012). Mining actionlet ensemble for action recognition with depth cameras. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247813

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