Realistic action recognition via sparsely-constructed Gaussian processes
We propose a realistic action recognition framework based on Gaussian processes (GPs). An ?1 construction and a local approximation covariance weight updating method are proposed for GPs. Our GPs are demonstrated to be robust to data noise, automatically sparse and adaptive to the neighborhood.
action recognition gaussian processes ?1 construction local approximation