On Destination Prediction Based on Markov Bridging Distributions
Accepted version
Peer-reviewed
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Abstract
This letter presents an alternative, more consistent, construction for bridging distributions, which enables inferring the destination of a tracked object from the available partial sensory observations. Two algorithms are then introduced to sequentially estimate the probability of all possible endpoints within a generic Bayesian framework. They capture the influence of intended destination on the object’s motion via suitably adapted stochastic models. Whilst the bridging approach has low training requirements, the proposed formulation can lead to more efficient predictors, e.g. around 65% less computations for certain models. Synthetic and real data is used to illustrate the effectiveness of the introduced algorithms.
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Keywords
Intent inference, tracking, Kalman filter
Journal Title
IEEE Signal Processing Letters
Conference Name
Journal ISSN
1070-9908
1558-2361
1558-2361
Volume Title
26
Publisher
Institute of Electrical and Electronics Engineers (IEEE)