Whole body motion primitive segmentation from monocular video

@article{Kulic2009WholeBM,
  title={Whole body motion primitive segmentation from monocular video},
  author={Dana Kulic and Dongheui Lee and Yoshihiko Nakamura},
  journal={2009 IEEE International Conference on Robotics and Automation},
  year={2009},
  pages={3166-3172}
}
This paper proposes a novel approach for motion primitive segmentation from continuous full body human motion captured on monocular video. The proposed approach does not require a kinematic model of the person, nor any markers on the body. Instead, optical flow computed directly in the image plane is used to estimate the location of segment points. The approach is based on detecting tracking features in the image based on the Shi and Thomasi algorithm [1]. The optical flow at each feature point… CONTINUE READING

Citations

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SHOWING 1-10 OF 10 CITATIONS

Autonomous motion primitive segmentation of actions for incremental imitative learning of humanoid

  • 2014 IEEE Symposium on Robotic Intelligence in Informationally Structured Space (RiiSS)
  • 2014
VIEW 1 EXCERPT
CITES METHODS

Comparative study of representations for segmentation of whole body human motion data

  • 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2009
VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 34 REFERENCES

Scaffolding on-line segmentation of full body human motion patterns

  • 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2008
VIEW 9 EXCERPTS

Humanoid Robot's Autonomous Acquisition of Proto-Symbols through Motion Segmentation

  • 2006 6th IEEE-RAS International Conference on Humanoid Robots
  • 2006
VIEW 4 EXCERPTS

Unsupervised probabilistic segmentation of motion data for mimesis modeling

  • ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.
  • 2005
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Good features to track

  • 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
  • 1994
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL