14. A system for human detection and a pose classification comprising: training image receiving means for receiving a training image from a set of training images; image dividing means for dividing the training image into at least one cell, each cell having at least one pixel; descriptor generating means for generating a descriptor for each of the at least one cell, the descriptor representing the cell based on a histogram; model generating means for generating a probabilistic model based on the set of training images, the probabilistic model representing statistics of the set of training images characterized in that contributions of each component of the probabilistic model combine additively; detecting means for detecting if a human is present in a test image based on the probabilistic model derived from the set of training images; and classifying means for classifying a pose of a detected human in the test image based on the probabilistic model and the test image.
19. The system of claim 14 wherein the model generating means for generating a probabilistic model comprises parameter learning means for learning model parameters based on at least one of Gibbs sampling, Rao-Blackwellised sampling, mean field, or variational inference.