G3D: A gaming action dataset and real time action recognition evaluation framework

@article{Bloom2012G3DAG,
  title={G3D: A gaming action dataset and real time action recognition evaluation framework},
  author={Victoria Bloom and Dimitrios Makris and Vasileios Argyriou},
  journal={2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  year={2012},
  pages={7-12}
}
In this paper a novel evaluation framework for measuring the performance of real-time action recognition methods is presented. The evaluation framework will extend the time-based event detection metric to model multiple distinct action classes. The proposed metric provides more accurate indications of the performance of action recognition algorithms for games and other similar applications since it takes into consideration restrictions related to time and consecutive repetitions. Furthermore, a… CONTINUE READING
Highly Influential
This paper has highly influenced 24 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 128 citations. REVIEW CITATIONS
78 Citations
17 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 78 extracted citations

128 Citations

02040'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 128 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 17 references

Imagery Library for Intelligent Detection Systems : The i-LIDS User Guide

  • Home Office
  • 2011.
  • 2011
Highly Influential
4 Excerpts

Kinect Gesture Detection using Machine Learning [Online]. Available: http://www.microsoft.com/download/en/detail s.aspx?id=28066

  • C. Marais
  • 2011
Highly Influential
5 Excerpts

Kinect Natural User Interface ( NUI ) Overview [ Online ]

  • I. Laptev M. Marszalek, C. Schmid
  • 2012

Kinect Gesture Detection using Machine Learning [ Online ]

  • C Marais
  • 2011

Similar Papers

Loading similar papers…