Bayesian Filtering with Online Gaussian Process Latent Variable Models

In this paper we present a novel non-parametric approach to Bayesian filtering, where the prediction and observation models are learned in an online fashion. Our approach is able to handle multimodal distributions over both models by employing a mixture model representation with Gaussian Processes (GP) based components. To cope with the increasing complexity of the estimation process, we explore two computationally efficient GP variants, sparse online GP and local GP, which help to manage computation requirements for each mixture component. Our experiments demonstrate that our approach can track human motion much more accurately than existing approaches that learn the prediction and observation models offline and do not update these models with the incoming data stream.

Authors

• Yali Wang
• Marcus A. Brubaker
• Brahim Chaib-draa
• Raquel Urtasun

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