  Differentiable particle filters provide a flexible mechanism to adaptively
train dynamic and measurement models by learning from observed data. However,
most existing differentiable particle filters are within the bootstrap particle
filtering framework and fail to incorporate the information from latest
observations to construct better proposals. In this paper, we utilize
conditional normalizing flows to construct proposal distributions for
differentiable particle filters, enriching the distribution families that the
proposal distributions can represent. In addition, normalizing flows are
incorporated in the construction of the dynamic model, resulting in a more
expressive dynamic model. We demonstrate the performance of the proposed
conditional normalizing flow-based differentiable particle filters in a visual
tracking task.
