Titlebook: Stochastic Algorithms for Visual Tracking; Probabilistic Modell John MacCormick Book 2002 Springer-Verlag London Limited 2002 Importance Sa

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Conclusion?,ented, together with some methods of analysing the performance of particle filters, and a technique termed partitioned sampling which can dramatically improve the performance of particle filters in an important class of tracking problems.

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A probabilistic exclusion principle for multiple objects,tal problem to be addressed is demonstrated in Figure 6.1. Two instantiations of the same tracking algorithm, with different initial conditions, are used to track two targets simultaneously. When one target passes close to the other, both tracking algorithms are attracted to the single target which

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Partitioned sampling,distribution can be very large indeed. This is of particular concern when tracking multiple objects, since the number of dimensions in the state space is proportional to the number of objects. Fortunately, “partitioned sampling” significantly reduces this curse of dimensionality. It is the statistic

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Conclusion?,me” (Eliot, 1942). The book essentially outlined a set of models termed contour likelihoods, which can be used for performing statistical inference in localisation and tracking problems. The tracking methodology was a technique called particle filtering. The basic theory of particle filters was pres

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