Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Mining Actionlet Ensemble for Action Recognition with Depth Cameras

Jiang Wang, Zicheng Liu, Ying Wu, and Junsong Yuan


Human action recognition is an important yet challeng- ing 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 cap- tured by the depth cameras are very noisy and the 3D posi- tions of the tracked joints may be completely wrong if seri- ous occlusions occur, which increases the intra-class vari- ations in the actions. In this paper, an actionlet ensem- ble 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 misalign- ments, and capable of characterizing both the human mo- tion and the human-object interactions. The proposed ap- proach is evaluated on two challenging action recognition datasets captured by commodity depth cameras, and an- other dataset captured by a MoCap system. The experimen- tal evaluations show that the proposed approach achieves superior performance to the state of the art algorithms.


Publication typeProceedings
PublisherIEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
> Publications > Mining Actionlet Ensemble for Action Recognition with Depth Cameras