Human Action Recognition Using APJ3D and Random Forests

@article{Gan2013HumanAR,
  title={Human Action Recognition Using APJ3D and Random Forests},
  author={Ling Gan and Fu Chen},
  journal={JSW},
  year={2013},
  volume={8},
  pages={2238-2245}
}
Human action recognition is an important yet challenging task. In this paper, a simple and efficient method based on random forests is proposed for human action recognition. First, we extract the 3D skeletal joint locations from depth images. The APJ3D computed from the action depth image sequences by employing the 3D joint position features and the 3D joint angle features, and then clustered into K-means algorithm, which represent the typical postures of actions. By employing the improved… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-9 of 9 extracted citations

Action Recognition Using Local Joints Structure and Histograms of 3D Joints

2014 Tenth International Conference on Computational Intelligence and Security • 2014
View 7 Excerpts
Highly Influenced

Human activity recognition in the context of industrial human-robot interaction

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific • 2014
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 20 references

Mining actionlet ensemble for action recognition with depth cameras

2012 IEEE Conference on Computer Vision and Pattern Recognition • 2012
View 12 Excerpts
Highly Influenced

Real-Time Human Pose Recognition in Parts from Single Depth Images

Machine Learning for Computer Vision • 2013
View 2 Excerpts

Q.S.Tang, H.Jin, Y.Qiu and Y.Guo, “Eye Location Based on Adaboost and Random Forests

X. D. Zhang
Journal of Software, • 2012
View 2 Excerpts

View invariant human action recognition using histograms of 3D joints

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops • 2012
View 1 Excerpt

Islam, “Performance of PCA Based Semi-supervised Learning in Face Recognition Using MPEG-7 Edge Histogram Descriptor

S. Rahman, S.Motahar, M.M.A.A.Farooq
Journal of Multimedia, • 2011
View 1 Excerpt

Human action recognition with extremities as semantic posture representation

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops • 2009
View 1 Excerpt

Similar Papers

Loading similar papers…