Representing Spatial Trajectories as Distributions

Dídac Surís; Carl Vondrick

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arXiv · 2210.01322

Representing Spatial Trajectories as Distributions

Dídac Surís·Carl Vondrick

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Abstract

We introduce a representation learning framework for spatial trajectories. We represent partial observations of trajectories as probability distributions in a learned latent space, which characterize the uncertainty about unobserved parts of the trajectory. Our framework allows us to obtain samples from a trajectory for any continuous point in time, both interpolating and extrapolating. Our flexible approach supports directly modifying specific attributes of a trajectory, such as its pace, as well as combining different partial observations into single representations. Experiments show our method's advantage over baselines in prediction tasks.

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Dídac Surís, Carl Vondrick. 2022-10-04. Representing Spatial Trajectories as Distributions. https://arxiv.org/abs/2210.01322

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