1. A pedestrian tracking method provided in time series with a certain pedestrian region appearing in images captured continuously in time for predicting and associating motion of the pedestrian region with time direction by use of a Monte Carlo filter so as to track the pedestrian region, comprising the steps of: building a skeleton model of a pedestrian model with multiple nodes and multiple sticks connecting the nodes, and defining, in a state space of a multidimensional space representing points in one-to-one correspondence with states of the skeleton model, particles at the points provided with realization probability attributes of the states represented by the points of the state space, and further setting multiple particles of the skeleton model as an initial state in the state space; predicting, for a set of particles set in the state space at a first time, a set of particles representing a state at a second time after the first time using Monte Carlo filter method; converting a pedestrian region given at the second time to a binary image, and subjecting the binary image to distance transform so as to generate observed data comprised of the distance-transformed image; comparing each skeleton model predicted in the prediction step with the distance-transformed image so as to calculate a likelihood of each state of the skeleton model at the second time; and sampling a set of particles of the skeleton model in the state space based on the respective likelihoods of the skeleton model calculated in the above step, and setting the sampled set of particles as a new set of particles in the state space at the second time, wherein the pedestrian region is tracked by performing, at each time increment, the steps from the above step of predicting the set of particles to the step of setting the sampled set of particles as the new set of particles so as to predict and associate, with time direction, the motion of the particles of the skeleton model in the state space.