1. A method for tracking a number of objects or object parts in image sequences, comprising following a Bayesian-like approach to object tracking, computing, at each time a new image is available, a probability distribution over target configurations for that time, said Bayesian-like approach to object tracking comprising: computing a probability distribution for the previous image, at time (t−1), and propagating the probability distribution to the new image, at time (t), according to a probabilistic model of target dynamics, to obtain a predicted, or prior, distribution at time (t); and aligning the predicted distribution, at time (t), with the evidence contained in the new image, at time (t), according to a probabilistic model of visual likelihood, to obtain a posterior distribution at time (t); wherein aligning the predicted distribution, at time (t), of a hypothetic target configuration associated with a new frame by performing image analysis includes, identifying the image portion in which the target under consideration is expected to be visible under the current configuration, by using a shape rendering function, assigning a probability value to each pixel of the identified image portion, which is computed as a probability of the target under consideration being expected to be visible under the current configuration being occluded by another target, the probability of the target under consideration being expected to be visible under the current configuration being occluded by another target being derived from prior distributions and shape models of other targets, computing a degree of dissimilarity between visual features extracted from the identified image portion and a corresponding characterization, or appearance model, of the target, an importance, or weight, of the different pixels being calculated by the probability of the target under consideration being expected to be visible under the current configuration being occluded, adding, to the value of degree of dissimilarity, a further dissimilarity term per each other tracked target, each further dissimilarity term being computed in form of an expectation, under a prior distribution, of the dissimilarity value, the expectation of dissimilarity value being calculated on configurations that map closer to an image recording device than the target under analysis wherein the target, currently under consideration, being neglected, and calculating a distribution value, assigned to the hypothetic target configuration of the target under analysis, by multiplying a prior value with a negative exponential of an overall dissimilarity score.