Hewlett-packard Development Company, L.p.

Vladimir Pavlović

James Matthew Rehg

Ashutosh Garg

Simon Kasif

A target in a sequence of measurements is tracked by modeling the target with a switching linear dynamic system (SLDS) having a plurality of dynamic models. Each dynamic model is associated with a switching state such that a model is selected when its associated switching state is true. A set of continuous state estimates is determined for a given measurement, and for each possible switching state. A state transition record is then determined by determining and recording, for a given measurement and for each possible switching state, an optimal previous switching state, based on the measurement sequence, where the optimal previous switching state optimizes a transition probability based on the set of continuous state estimates. A measurement model of the target is fitted to the measurement sequence. The measurement model is the description of the influence of the state on the measurement. It couples what is observed to the estimated target. Finally, a trajectory of the target is estimated from the measurement model fitting, the state transition record and parameters of the SLDS, where the estimated trajectory is a sequence of continuous state estimates of the target which correspond to the measurement sequence. The set of continuous state estimates is preferably obtained through Viterbi prediction. The optimal previous switching state can be an optimal prior switching state, or can be an optimal posterior switching state.

Method for visual tracking using switching linear dynamic systems models

A method for synthesizing a sequence includes defining a switching linear dynamic system (SLDS) with a plurality of dynamic systems. In a Viterbi-based method, a state transition record for a training sequence is determined. The corresponding sequence of switching states is determined by backtracking through the state transition record. Parameters of dynamic models are learned in response to the determined sequence of switching states, and a new data sequence is synthesized, based on the dynamic models whose parameters have been learned. In a variational-based method, the switching state at a particular instance is determined by a switching model. The dynamic models are decoupled from the switching model, and parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. Similar methods are used to interpolate from an input sequence.

Method for motion synthesis and interpolation using switching linear dynamic system models

Portions of an input measurement sequence are classified into a plurality of regimes by associating each of a plurality of dynamic models with one a switching state such that a model is selected when its associated switching state is true. In a Viterbi-based method, a state transition record is determined, based on the input sequence. A switching state sequence is determined by backtracking through the state transition record. Finally, portions of the input sequence are classified into different regimes, responsive to the switching state sequence. In a variational-based method, the switching state at a particular instance is also determined by a switching model. The dynamic model is then decoupled from the switching model. Parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. A state of the decoupled dynamic model corresponding to a measurement at the particular instance is estimated, responsive to the input sequence. Parameters of the decoupled switching model are then determined responsive to the dynamic state estimate. A probability is estimated for each possible switching state of the decoupled switching model. A switching state sequence is determined based on the estimated switching state probabilities. Finally, portions of the input sequence are classified into different regimes, responsive to the determined switching state sequence.

Method for motion classification using switching linear dynamic systems models

Computer based apparatus and method automates gene prediction in a subject genomic sequence. A plurality of expert systems provide preliminary or intermediate gene predictions. A Bayesian network combiner combines the intermediate gene predictions and forms a final gene prediction. The final gene prediction accounts for dependencies between individual expert systems and dependencies between adjacent parts of the subject genomic sequence.

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Melrose, MA, United States of America

Vladimir Pavlović