Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures | Zendy

Tatsuya Harada | Zendy; Taketoshi Mori | Zendy; Tomomasa Sato | Zendy

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Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures

Author(s) -

Tatsuya Harada,

Taketoshi Mori,

Tomomasa Sato

Publication year - 2005

Publication title -

journal of robotics and mechatronics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.257

H-Index - 19

eISSN - 1883-8049

pISSN - 0915-3942

DOI - 10.20965/jrm.2005.p0664

Subject(s) - kernel density estimation , quaternion , artificial intelligence , computer vision , estimator , motion (physics) , computer science , mathematics , motion capture , probability density function , euclidean distance , kernel (algebra) , statistics , geometry , combinatorics

We construct human posture probability density based on actual human motion measurement. Human postures in daily life were measured for two days by having subjects wear a mechanical motion capture device. Accumulated human postures were converted to unit quaternions to guarantee the uniqueness of posture representation. To represent probability density effectively, we propose eigenpostures for posture compression and use the kernel-based reduced set density estimator (RSDE) to reduce the number of posture samples and construction of posture probability density. Before compression, unit quaternions were converted to Euclidean space by logarithmic mapping. After conversion, postures were compressed in Euclidean space. Applying constructed human posture probability density for unlikely posture detection and motion segmentation, we verified its effectiveness for many different applications.

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