Probabilistic exemplar-based pattern tracking
First Claim
1. A method for automatic probabilistic pattern tracking comprising steps for:
- automatically learning a set of exemplars from at least one set of training data;
clustering the exemplars into more than one cluster of exemplars, with each cluster having a representative exemplar at a cluster center;
generating an observation likelihood function for each exemplar cluster based on a computed distance between the exemplars in each cluster;
providing the exemplar clusters, observation likelihood functions, and target data to a probabilistic tracking function; and
probabilistically tracking at least one pattern in the target data by using the exemplar clusters, observation likelihood functions, and target data to predict at least one target state.
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Abstract
The present invention involves a new system and method for probabilistic exemplar-based tracking of patterns or objects. Tracking is accomplished by first extracting a set of exemplars from training data. The exemplars are then clustered using conventional statistical techniques. Such clustering techniques include k-medoids clustering which is based on a distance function for determining the distance or similarity between the exemplars. A dimensionality for each exemplar cluster is then estimated and used for generating a probabilistic likelihood function for each exemplar cluster. Any of a number of conventional tracking algorithms is then used in combination with the exemplars and the probabilistic likelihood functions for tracking patterns or objects in a sequence of images, or in a space, or frequency domain.
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Citations
20 Claims
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1. A method for automatic probabilistic pattern tracking comprising steps for:
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automatically learning a set of exemplars from at least one set of training data;
clustering the exemplars into more than one cluster of exemplars, with each cluster having a representative exemplar at a cluster center;
generating an observation likelihood function for each exemplar cluster based on a computed distance between the exemplars in each cluster;
providing the exemplar clusters, observation likelihood functions, and target data to a probabilistic tracking function; and
probabilistically tracking at least one pattern in the target data by using the exemplar clusters, observation likelihood functions, and target data to predict at least one target state. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer-readable medium having computer executable instructions for generating a set of observation likelihood functions from a set of exemplars, said computer executable instructions comprising:
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deriving more than one exemplar from at least one set of training data to create a set of exemplars;
randomly selecting more than one exemplar from the set of exemplars;
iteratively clustering similar exemplars from the set of exemplars around the randomly selected exemplars to form an exemplar cluster for each of the randomly selected exemplars;
estimating a dimensionality for each of the exemplar clusters based on the computed minimum distances between exemplars in each exemplar cluster; and
computing an observation likelihood function for each exemplar cluster based on the dimensionality of each exemplar cluster. - View Dependent Claims (10, 11, 12, 13)
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14. A system for automatically tracking patterns in a set of tracking data, comprising using a computing device for:
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generating at least one set of clustered exemplars from a set of training data;
for each exemplar cluster, computing a distance between a representative exemplar at a center of each cluster and each of the other exemplars in that cluster;
using the computed distance to estimate an observation likelihood function for each cluster of exemplars; and
using the observation likelihood function for each cluster of exemplars to probabilistically track at least one pattern in at least one set of tracking data. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification