Abstract:
This letter presents an alternative, more consistent, construction for bridging distributions, which enables inferring the destination of a tracked object from the availa...Show MoreMetadata
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.
Published in: IEEE Signal Processing Letters ( Volume: 26, Issue: 11, November 2019)
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- IEEE Keywords
- Index Terms
- Destination Prediction ,
- Bayesian Framework ,
- Object Motion ,
- Dynamic Model ,
- Computational Complexity ,
- Arrival Time ,
- Uniform Prior ,
- Motion Model ,
- Numerical Approximation ,
- Linear Time-invariant ,
- Ornstein-Uhlenbeck Process ,
- Transition Density ,
- Dynamic Noise ,
- Matrix Inversion Lemma ,
- Accurate State Estimation ,
- Constant Velocity Model
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Destination Prediction ,
- Bayesian Framework ,
- Object Motion ,
- Dynamic Model ,
- Computational Complexity ,
- Arrival Time ,
- Uniform Prior ,
- Motion Model ,
- Numerical Approximation ,
- Linear Time-invariant ,
- Ornstein-Uhlenbeck Process ,
- Transition Density ,
- Dynamic Noise ,
- Matrix Inversion Lemma ,
- Accurate State Estimation ,
- Constant Velocity Model
- Author Keywords