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 the following steps: computing a probability distribution for the previous image, at time (t- 1 ), is propagated to the new image at time (t) according to a probabilistic model of target dynamics, obtaining 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, obaining a posterior distribution at time (t); wherein aligning said predicted distribution at time (t) of a hypothetic target configuration associated with a new frame by performing image analysis in the following way, the image portion in which the target under consideration is expected to be visible under the current configuration is identified, by using a shape rendering function, namely a user-defined shape model of the target that is rendered onto the image for the specific configuration under analysis, a probability value is assigned to each pixel of the identified image portion, which is computed as the probability of being occluded by another target, the probability being derived from other targets' prior distributions and their shape model, a degree of dissimilarity is computed, in a user-defined way, between visual features extracted from the identified image portion and a corresponding characterization, or appearance model, of the target, the importance, or weight, of the different pixels in this calculation being thereby calculated by said probability of being occluded, to said value of degree of dissimilarity, a further dissimilarity term per each other tracked target is added, each such other target-term being computed in form of the expectation, under its prior distribution, of the dissimilarity values calculated on those configurations that map closer to the camera (or to any other shooting or picture recording device used) than the one of the target under analysis, the target currently under consideration being neglected, and the distribution value assigned to the hypothesized configuration of the target under analysis is finally calculated by multiplying the prior value with the negative exponential of the overall dissimilarity score as obtained according to the previous two items.