| 摘要 |
A method for tracking a number of objects or object parts in image sequences utilizes a Bayesian-like approach to object tracking, computing, at each time a new image is available, a probability distribution over all possible target configurations for that time. The Bayesian-like approach to object tracking computes 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 distribution at time (t). The Bayesian-like approach to object tracking also aligns 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.
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