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In 3-D articulated human motion tracking, the curse of dimensionality renders commonly-used particle-filter-based approaches inefficient. Also, noisy image measurements and imperfect feature extraction call for strong motion prior. We propose to learn the correlation between the right-side and the left-side human motion using partial least square (PLS) regression. The correlation effectively constrains...
This paper proposes a new single-frame image up-conversion approach that uses prior information. The proposed method overcomes the drawbacks of the Kondo 2001 where the class membership of a local aperture depends only on the contents of itself, without taking any consideration of the contents of all the other training apertures, and all the features in an aperture share one single break point. We...
Inference in 3D articulated human body tracking is challenging due to the high dimensionality and nonlinearity of the parameter-space. We propose a particle filter with Rao-Blackwellisation which marginalizes part of the state variables by exploiting the correlation between the right-side and the left-side joint Euler angles. The correlation is naturally induced by the symmetric and repetitive patterns...
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