Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Human Gender and Motion Analysis with Ellipsoid and Logistic Regression Method

  • Received : 2016.06.20
  • Accepted : 2016.07.28
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.

Keywords

References

  1. A. J. Stoddart, P. Mrazek, D. Ewins, and D. Hynd "Marker based motion capture in biomedical applications," Motion Analysis and Tracking, 1999.
  2. Gait, "Recognition Based on Marker-less 3D Motion Capture," 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013.
  3. C. Stoll, N. Hasler, J. Gall, H.-P. Seidel, and C. Theobalt, "Fast articulated motion tracking using a sums of gaussians body model," in Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 951-958, 2011.
  4. K. Eguro, R. Bittner, and A. Forin "Random decision tree body part recognition using FPGAs" in 22nd International Conference on Field Programmable Logic and Applications (FPL), pp. 330-337, Aug 2012.
  5. https://developer.microsoft.com/enus/windows/kinect/hardware, June 2016.
  6. G. Chantas, N. Galatsanos, and A. Likas "Maximum a posteriori image restoration based on a new directional continuous edge image prior" in IEEE International Conference on Image Processing 2005, pp. 941-944, Sep. 2005.
  7. T. P. Minka "Algorithm for maximum-likelihood logistic regression", 2003.
  8. G. Marther and L. Murdoch "Gender discrimination in bilogical motion displays based on dynamics cues", Biological Science, pp. 273-279, 1994.
  9. Y. Zhu and K. Fujimura, "Constrained optimization for human pose estimation from depth sequences," ACCV 2007, pp. 408-418, 2007.
  10. R. Birdwhistell "kinesics and context", 1970.
  11. P. Zhang, K. Siu, J. Zhang, C. K. Liu, and J. Chai, "Leveraging depth cameras and wearable pressure sensors for full-body kinematics and dynamics capture," ACM Trans. Graph., vol. 33, no. 6, pp. 221:1-221:14, 2014.
  12. C. Stoll, N. Hasler, J. Gall, H.-P. Seidel, and C. Theobalt, "Fast articulated motion tracking using a sums of gaussians body model," in Computer Vision (ICCV), 2011 IEEE International Conference, pp. 951-958, 2011.
  13. S. Sridhar, A. Oulasvirta, and C. Theobalt, "Interactive markerless articulated hand motion tracking using RGB and depth data," in IEEE International Conference on Computer Vision, pp. 2456-2463, 2013.