Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100602
Title: Mining actionlet ensemble for action recognition with depth cameras
Authors: Wang, Jiang
Liu, Zicheng
Wu, Ying
Yuan, Junsong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: 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, 1290-1297.
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.
URI: https://hdl.handle.net/10356/100602
http://hdl.handle.net/10220/17897
DOI: http://dx.doi.org/10.1109/CVPR.2012.6247813
Rights: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/CVPR.2012.6247813].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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